Body, Heart, & Mind (Weisman, Dweck, & Markman, submitted)
Data preparation
NAs introduced by coercionNAs introduced by coercionNAs introduced by coercionNAs introduced by coercionNAs introduced by coercionNAs introduced by coercion‘-’ not meaningful for factorsJoining, by = c("study", "subid", "target", "happy", "depressed", "fear", "angry", "calm", "sounds", "seeing", "temperature", "odors", "depth", "computations", "thoughts", "reasoning", "remembering", "beliefs", "hungry", "tired", "pain", "nauseated", "safe", "love", "recognizing", "communicating", "guilt", "disrespected", "free_will", "choices", "self_restraint", "intentions", "goal", "conscious", "self_aware", "desires", "embarrassed", "emo_recog", "joy", "morality", "personality", "pleasure", "pride", "CATCH")
Joining, by = c("study", "subid")Analysis plan
For all studies we conduct exploratory factor analyses using Pearson correlations to find minimum residual solutions.
For each study, we first examine maximal unrotated and rotated solutions. To determine the maximum number of factors to extract, we use the following rule of thumb: With \(p\) observations per participant, we can extract a maximum of \(k\) factors, where \((p-k)*2 > p+k\), i.e., \(k < p/3\). Thus, with 40 mental capacity items, we can extract a maximum of 13 factors.
To determine how many factors to retain, we use the following preset retention criteria, considering the unrotated maximal solution (unless otherwise noted):
- Each factor must have an eigenvalue >1.0.
- Each factor must individually account for >5% of the total variance in the maximal model.
- After rotation, each factor must be the dominant factor (i.e., the factor with the highest factor loading) for ≥1 mental capacity item.
We then examine and interpret varimax-rotated solutions, extracting only the number of factors that meet these criteria.
Note: For Studies 1-2, we initially planned to conduct dimension reduction analyses for each condition (beetle vs. robot) separately. However, we now consider this analysis plan to have been fundamentally flawed: Each of these separate analyses is only capable of surfacing factors that highlight substantial disagreement among participants within that condition thus failing to capture key differences in attributions of mental capacities to beetles vs. robots, with no formal means of synthesizing results across conditions. Nonetheless, the results of these analyses are generally consistent with the findings reported here: The most prominent and reliable finding within each condition is that participants distinguish between emotional and perceptual varieties of experience. See https://osf.io/zd3mu for the preregistered analyses, including analysis scripts.
Study 1
Design: 2 conditions (beetle, robot), between-subjects Date conducted: 2015-12-15
Demographics
NAs introduced by coercionJoining, by = c("condition", "min_age", "max_age", "median_age", "mean_age", "sd_age")
Column `condition` joining factor and character vector, coercing into character vectorExploratory factor analysis
Step 1: Run maximal EFA (without and with rotation)
Factor Analysis using method = minres
Call: fa(r = d1_all, nfactors = 13, rotate = "none", fm = "minres",
cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11
happy 0.85 -0.08 -0.12 0.07 -0.21 0.09 -0.10 0.09 -0.03 0.00 0.01
depressed 0.80 0.04 -0.32 0.16 -0.13 -0.11 -0.06 -0.02 0.01 -0.04 -0.09
fear 0.76 -0.38 0.20 0.02 0.03 -0.01 0.10 0.02 -0.07 -0.09 0.11
angry 0.80 -0.13 -0.07 0.11 -0.11 -0.07 -0.02 0.03 0.02 -0.05 0.08
calm 0.77 -0.16 0.11 -0.02 -0.10 0.13 0.10 -0.07 -0.01 -0.04 -0.03
sounds 0.17 0.39 0.48 0.32 -0.05 0.23 -0.04 0.01 0.10 -0.05 0.05
seeing 0.35 0.20 0.60 0.17 0.14 -0.09 -0.08 0.03 -0.05 0.03 -0.02
temperature 0.27 0.33 0.59 0.19 0.08 0.03 0.03 0.22 0.11 -0.03 -0.02
odors 0.41 0.03 0.50 0.23 0.13 0.05 -0.09 -0.10 -0.02 -0.02 -0.20
depth 0.31 0.41 0.38 0.07 0.01 -0.01 -0.04 0.01 0.07 0.11 0.12
computations -0.25 0.83 -0.14 0.05 -0.15 -0.01 0.06 0.06 0.05 -0.01 0.07
thoughts 0.78 0.03 -0.02 -0.17 -0.02 0.04 -0.15 0.00 -0.03 0.08 0.00
reasoning 0.44 0.61 0.02 -0.17 0.02 -0.02 -0.10 0.04 -0.10 0.04 -0.04
remembering 0.18 0.69 0.28 -0.04 -0.14 -0.01 0.12 0.03 -0.07 -0.07 -0.04
beliefs 0.63 0.28 -0.37 0.00 0.21 0.02 0.01 0.03 0.03 0.04 -0.04
hungry 0.60 -0.62 0.33 0.03 0.17 -0.01 -0.02 0.00 -0.02 0.00 -0.03
tired 0.74 -0.37 0.25 0.00 0.01 -0.06 0.05 -0.03 0.08 -0.04 0.08
pain 0.69 -0.53 0.32 0.02 0.13 -0.02 -0.02 0.01 -0.07 0.02 0.08
nauseated 0.81 -0.19 0.01 0.15 -0.21 -0.03 0.08 -0.19 0.01 0.07 -0.05
safe 0.76 -0.23 0.14 -0.03 -0.07 0.19 0.26 0.06 -0.16 -0.02 -0.01
love 0.82 0.07 -0.32 0.09 -0.05 0.01 -0.06 0.12 -0.01 0.01 -0.01
recognizing 0.20 0.78 0.15 0.05 -0.10 -0.02 0.02 -0.15 -0.08 0.03 0.04
communicating 0.29 0.55 0.28 0.00 0.03 -0.05 0.09 -0.12 -0.16 0.11 0.13
guilt 0.73 0.11 -0.39 0.13 0.08 -0.01 0.00 -0.06 0.04 -0.01 -0.09
disrespected 0.72 0.13 -0.37 0.20 0.08 -0.17 0.18 -0.08 0.06 -0.12 -0.01
free_will 0.75 -0.26 0.11 -0.12 -0.05 0.00 -0.04 0.09 -0.06 -0.07 -0.12
choices 0.46 0.35 0.37 -0.28 0.00 -0.13 0.06 0.10 -0.12 -0.04 -0.16
self_restraint 0.63 0.26 -0.08 -0.16 0.07 0.16 0.02 -0.23 0.07 -0.12 -0.07
intentions 0.68 -0.03 0.16 -0.29 -0.03 0.01 0.11 0.07 0.29 -0.03 0.03
goal 0.35 0.44 0.34 -0.15 -0.04 -0.27 -0.02 -0.03 0.14 -0.01 -0.07
conscious 0.76 -0.23 0.13 -0.06 0.02 0.07 -0.05 -0.09 0.04 0.10 0.01
self_aware 0.74 -0.01 0.03 -0.11 0.01 0.01 -0.11 -0.08 0.05 0.05 0.05
desires 0.78 -0.22 0.09 -0.17 -0.02 -0.08 0.01 -0.02 0.02 0.08 0.05
embarrassed 0.71 0.16 -0.49 0.15 0.17 -0.10 0.10 0.13 -0.04 0.05 0.01
emo_recog 0.61 0.41 -0.25 -0.05 0.21 0.05 -0.07 -0.04 0.01 0.10 0.05
joy 0.86 -0.03 -0.17 0.02 -0.23 0.07 -0.16 0.08 0.00 0.09 -0.05
morality 0.48 0.47 -0.22 -0.15 0.14 0.28 0.09 0.07 0.04 0.04 -0.01
personality 0.70 0.31 -0.15 -0.07 0.06 -0.01 -0.21 -0.01 -0.09 -0.33 0.17
pleasure 0.82 -0.25 0.10 -0.03 -0.07 -0.11 0.04 0.04 0.01 0.07 0.10
pride 0.79 0.14 -0.39 0.11 0.04 -0.05 0.05 0.05 -0.03 0.07 -0.02
MR12 MR13 h2 u2 com
happy 0.06 -0.15 0.84 0.16 1.4
depressed -0.05 -0.03 0.81 0.19 1.6
fear 0.02 0.06 0.80 0.20 1.8
angry 0.02 0.08 0.71 0.29 1.2
calm 0.01 0.06 0.68 0.32 1.3
sounds 0.04 0.10 0.60 0.40 3.9
seeing 0.02 -0.04 0.59 0.41 2.3
temperature -0.06 0.04 0.64 0.36 2.9
odors 0.06 0.02 0.55 0.45 3.2
depth -0.08 -0.15 0.48 0.52 3.8
computations 0.01 0.04 0.80 0.20 1.4
thoughts 0.03 0.11 0.69 0.31 1.2
reasoning -0.11 0.04 0.63 0.37 2.3
remembering -0.03 -0.02 0.62 0.38 1.7
beliefs -0.03 0.05 0.67 0.33 2.4
hungry -0.03 -0.07 0.90 0.10 2.7
tired -0.06 -0.09 0.78 0.22 1.9
pain -0.03 0.00 0.89 0.11 2.5
nauseated -0.28 0.06 0.90 0.10 1.8
safe 0.03 -0.05 0.79 0.21 1.8
love 0.07 -0.06 0.82 0.18 1.4
recognizing 0.04 0.05 0.71 0.29 1.4
communicating 0.14 -0.04 0.57 0.43 3.0
guilt 0.08 -0.07 0.75 0.25 1.8
disrespected 0.07 0.00 0.81 0.19 2.2
free_will 0.08 0.16 0.73 0.27 1.6
choices -0.06 -0.06 0.63 0.37 4.6
self_restraint 0.02 -0.10 0.62 0.38 2.3
intentions 0.02 0.03 0.68 0.32 2.0
goal 0.06 -0.03 0.56 0.44 4.3
conscious 0.01 0.00 0.67 0.33 1.4
self_aware 0.10 0.02 0.60 0.40 1.2
desires 0.01 0.07 0.71 0.29 1.4
embarrassed -0.04 -0.01 0.86 0.14 2.4
emo_recog -0.07 0.05 0.68 0.32 2.7
joy 0.01 -0.08 0.87 0.13 1.4
morality -0.04 -0.04 0.64 0.36 3.7
personality -0.09 -0.01 0.81 0.19 2.5
pleasure 0.04 0.00 0.79 0.21 1.3
pride 0.02 0.07 0.82 0.18 1.7
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11 MR12
SS loadings 16.55 5.24 3.42 0.75 0.51 0.42 0.35 0.31 0.28 0.26 0.24 0.21
Proportion Var 0.41 0.13 0.09 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Var 0.41 0.54 0.63 0.65 0.66 0.67 0.68 0.69 0.70 0.70 0.71 0.71
Proportion Explained 0.58 0.18 0.12 0.03 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Proportion 0.58 0.76 0.88 0.90 0.92 0.94 0.95 0.96 0.97 0.98 0.99 0.99
MR13
SS loadings 0.18
Proportion Var 0.00
Cumulative Var 0.72
Proportion Explained 0.01
Cumulative Proportion 1.00
Mean item complexity = 2.2
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 36.39 with Chi Square of 14185
The degrees of freedom for the model are 338 and the objective function was 1.03
The root mean square of the residuals (RMSR) is 0.01
The df corrected root mean square of the residuals is 0.01
The harmonic number of observations is 405 with the empirical chi square 57.84 with prob < 1
The total number of observations was 405 with Likelihood Chi Square = 390.74 with prob < 0.025
Tucker Lewis Index of factoring reliability = 0.991
RMSEA index = 0.024 and the 90 % confidence intervals are 0.008 0.028
BIC = -1638.58
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5 MR6 MR7
Correlation of scores with factors 0.99 0.98 0.96 0.84 0.85 0.76 0.78
Multiple R square of scores with factors 0.99 0.95 0.92 0.71 0.72 0.58 0.61
Minimum correlation of possible factor scores 0.97 0.90 0.85 0.41 0.44 0.17 0.22
MR8 MR9 MR10 MR11 MR12 MR13
Correlation of scores with factors 0.74 0.69 0.73 0.68 0.72 0.65
Multiple R square of scores with factors 0.55 0.48 0.53 0.46 0.52 0.42
Minimum correlation of possible factor scores 0.10 -0.04 0.06 -0.09 0.04 -0.17
Factor Analysis using method = minres
Call: fa(r = d1_all, nfactors = 13, rotate = "none", fm = "minres",
cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11
happy 0.85 -0.08 -0.12 0.07 -0.21 0.09 -0.10 0.09 -0.03 0.00 0.01
depressed 0.80 0.04 -0.32 0.16 -0.13 -0.11 -0.06 -0.02 0.01 -0.04 -0.09
fear 0.76 -0.38 0.20 0.02 0.03 -0.01 0.10 0.02 -0.07 -0.09 0.11
angry 0.80 -0.13 -0.07 0.11 -0.11 -0.07 -0.02 0.03 0.02 -0.05 0.08
calm 0.77 -0.16 0.11 -0.02 -0.10 0.13 0.10 -0.07 -0.01 -0.04 -0.03
sounds 0.17 0.39 0.48 0.32 -0.05 0.23 -0.04 0.01 0.10 -0.05 0.05
seeing 0.35 0.20 0.60 0.17 0.14 -0.09 -0.08 0.03 -0.05 0.03 -0.02
temperature 0.27 0.33 0.59 0.19 0.08 0.03 0.03 0.22 0.11 -0.03 -0.02
odors 0.41 0.03 0.50 0.23 0.13 0.05 -0.09 -0.10 -0.02 -0.02 -0.20
depth 0.31 0.41 0.38 0.07 0.01 -0.01 -0.04 0.01 0.07 0.11 0.12
computations -0.25 0.83 -0.14 0.05 -0.15 -0.01 0.06 0.06 0.05 -0.01 0.07
thoughts 0.78 0.03 -0.02 -0.17 -0.02 0.04 -0.15 0.00 -0.03 0.08 0.00
reasoning 0.44 0.61 0.02 -0.17 0.02 -0.02 -0.10 0.04 -0.10 0.04 -0.04
remembering 0.18 0.69 0.28 -0.04 -0.14 -0.01 0.12 0.03 -0.07 -0.07 -0.04
beliefs 0.63 0.28 -0.37 0.00 0.21 0.02 0.01 0.03 0.03 0.04 -0.04
hungry 0.60 -0.62 0.33 0.03 0.17 -0.01 -0.02 0.00 -0.02 0.00 -0.03
tired 0.74 -0.37 0.25 0.00 0.01 -0.06 0.05 -0.03 0.08 -0.04 0.08
pain 0.69 -0.53 0.32 0.02 0.13 -0.02 -0.02 0.01 -0.07 0.02 0.08
nauseated 0.81 -0.19 0.01 0.15 -0.21 -0.03 0.08 -0.19 0.01 0.07 -0.05
safe 0.76 -0.23 0.14 -0.03 -0.07 0.19 0.26 0.06 -0.16 -0.02 -0.01
love 0.82 0.07 -0.32 0.09 -0.05 0.01 -0.06 0.12 -0.01 0.01 -0.01
recognizing 0.20 0.78 0.15 0.05 -0.10 -0.02 0.02 -0.15 -0.08 0.03 0.04
communicating 0.29 0.55 0.28 0.00 0.03 -0.05 0.09 -0.12 -0.16 0.11 0.13
guilt 0.73 0.11 -0.39 0.13 0.08 -0.01 0.00 -0.06 0.04 -0.01 -0.09
disrespected 0.72 0.13 -0.37 0.20 0.08 -0.17 0.18 -0.08 0.06 -0.12 -0.01
free_will 0.75 -0.26 0.11 -0.12 -0.05 0.00 -0.04 0.09 -0.06 -0.07 -0.12
choices 0.46 0.35 0.37 -0.28 0.00 -0.13 0.06 0.10 -0.12 -0.04 -0.16
self_restraint 0.63 0.26 -0.08 -0.16 0.07 0.16 0.02 -0.23 0.07 -0.12 -0.07
intentions 0.68 -0.03 0.16 -0.29 -0.03 0.01 0.11 0.07 0.29 -0.03 0.03
goal 0.35 0.44 0.34 -0.15 -0.04 -0.27 -0.02 -0.03 0.14 -0.01 -0.07
conscious 0.76 -0.23 0.13 -0.06 0.02 0.07 -0.05 -0.09 0.04 0.10 0.01
self_aware 0.74 -0.01 0.03 -0.11 0.01 0.01 -0.11 -0.08 0.05 0.05 0.05
desires 0.78 -0.22 0.09 -0.17 -0.02 -0.08 0.01 -0.02 0.02 0.08 0.05
embarrassed 0.71 0.16 -0.49 0.15 0.17 -0.10 0.10 0.13 -0.04 0.05 0.01
emo_recog 0.61 0.41 -0.25 -0.05 0.21 0.05 -0.07 -0.04 0.01 0.10 0.05
joy 0.86 -0.03 -0.17 0.02 -0.23 0.07 -0.16 0.08 0.00 0.09 -0.05
morality 0.48 0.47 -0.22 -0.15 0.14 0.28 0.09 0.07 0.04 0.04 -0.01
personality 0.70 0.31 -0.15 -0.07 0.06 -0.01 -0.21 -0.01 -0.09 -0.33 0.17
pleasure 0.82 -0.25 0.10 -0.03 -0.07 -0.11 0.04 0.04 0.01 0.07 0.10
pride 0.79 0.14 -0.39 0.11 0.04 -0.05 0.05 0.05 -0.03 0.07 -0.02
MR12 MR13 h2 u2 com
happy 0.06 -0.15 0.84 0.16 1.4
depressed -0.05 -0.03 0.81 0.19 1.6
fear 0.02 0.06 0.80 0.20 1.8
angry 0.02 0.08 0.71 0.29 1.2
calm 0.01 0.06 0.68 0.32 1.3
sounds 0.04 0.10 0.60 0.40 3.9
seeing 0.02 -0.04 0.59 0.41 2.3
temperature -0.06 0.04 0.64 0.36 2.9
odors 0.06 0.02 0.55 0.45 3.2
depth -0.08 -0.15 0.48 0.52 3.8
computations 0.01 0.04 0.80 0.20 1.4
thoughts 0.03 0.11 0.69 0.31 1.2
reasoning -0.11 0.04 0.63 0.37 2.3
remembering -0.03 -0.02 0.62 0.38 1.7
beliefs -0.03 0.05 0.67 0.33 2.4
hungry -0.03 -0.07 0.90 0.10 2.7
tired -0.06 -0.09 0.78 0.22 1.9
pain -0.03 0.00 0.89 0.11 2.5
nauseated -0.28 0.06 0.90 0.10 1.8
safe 0.03 -0.05 0.79 0.21 1.8
love 0.07 -0.06 0.82 0.18 1.4
recognizing 0.04 0.05 0.71 0.29 1.4
communicating 0.14 -0.04 0.57 0.43 3.0
guilt 0.08 -0.07 0.75 0.25 1.8
disrespected 0.07 0.00 0.81 0.19 2.2
free_will 0.08 0.16 0.73 0.27 1.6
choices -0.06 -0.06 0.63 0.37 4.6
self_restraint 0.02 -0.10 0.62 0.38 2.3
intentions 0.02 0.03 0.68 0.32 2.0
goal 0.06 -0.03 0.56 0.44 4.3
conscious 0.01 0.00 0.67 0.33 1.4
self_aware 0.10 0.02 0.60 0.40 1.2
desires 0.01 0.07 0.71 0.29 1.4
embarrassed -0.04 -0.01 0.86 0.14 2.4
emo_recog -0.07 0.05 0.68 0.32 2.7
joy 0.01 -0.08 0.87 0.13 1.4
morality -0.04 -0.04 0.64 0.36 3.7
personality -0.09 -0.01 0.81 0.19 2.5
pleasure 0.04 0.00 0.79 0.21 1.3
pride 0.02 0.07 0.82 0.18 1.7
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11 MR12
SS loadings 16.55 5.24 3.42 0.75 0.51 0.42 0.35 0.31 0.28 0.26 0.24 0.21
Proportion Var 0.41 0.13 0.09 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Var 0.41 0.54 0.63 0.65 0.66 0.67 0.68 0.69 0.70 0.70 0.71 0.71
Proportion Explained 0.58 0.18 0.12 0.03 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Proportion 0.58 0.76 0.88 0.90 0.92 0.94 0.95 0.96 0.97 0.98 0.99 0.99
MR13
SS loadings 0.18
Proportion Var 0.00
Cumulative Var 0.72
Proportion Explained 0.01
Cumulative Proportion 1.00
Mean item complexity = 2.2
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 36.39 with Chi Square of 14185
The degrees of freedom for the model are 338 and the objective function was 1.03
The root mean square of the residuals (RMSR) is 0.01
The df corrected root mean square of the residuals is 0.01
The harmonic number of observations is 405 with the empirical chi square 57.84 with prob < 1
The total number of observations was 405 with Likelihood Chi Square = 390.74 with prob < 0.025
Tucker Lewis Index of factoring reliability = 0.991
RMSEA index = 0.024 and the 90 % confidence intervals are 0.008 0.028
BIC = -1638.58
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5 MR6 MR7
Correlation of scores with factors 0.99 0.98 0.96 0.84 0.85 0.76 0.78
Multiple R square of scores with factors 0.99 0.95 0.92 0.71 0.72 0.58 0.61
Minimum correlation of possible factor scores 0.97 0.90 0.85 0.41 0.44 0.17 0.22
MR8 MR9 MR10 MR11 MR12 MR13
Correlation of scores with factors 0.74 0.69 0.73 0.68 0.72 0.65
Multiple R square of scores with factors 0.55 0.48 0.53 0.46 0.52 0.42
Minimum correlation of possible factor scores 0.10 -0.04 0.06 -0.09 0.04 -0.17
[1] 3[1] 3Step 2: Run EFA with varimax rotation
Factor Analysis using method = minres
Call: fa(r = d1_all, nfactors = nfactors_d1_all, rotate = chosenRotType,
fm = "minres", cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 h2 u2 com
happy 0.57 0.63 0.09 0.74 0.26 2.0
depressed 0.39 0.76 0.03 0.73 0.27 1.5
fear 0.83 0.26 0.06 0.76 0.24 1.2
angry 0.60 0.55 0.08 0.67 0.33 2.0
calm 0.66 0.40 0.17 0.63 0.37 1.8
sounds 0.06 -0.05 0.61 0.38 0.62 1.0
seeing 0.35 -0.08 0.63 0.52 0.48 1.6
temperature 0.21 -0.07 0.68 0.51 0.49 1.2
odors 0.45 -0.02 0.45 0.40 0.60 2.0
depth 0.10 0.11 0.62 0.41 0.59 1.1
computations -0.73 0.21 0.42 0.76 0.24 1.8
thoughts 0.51 0.55 0.21 0.61 0.39 2.3
reasoning -0.07 0.49 0.56 0.55 0.45 2.0
remembering -0.20 0.18 0.71 0.58 0.42 1.3
beliefs 0.11 0.77 0.12 0.61 0.39 1.1
hungry 0.92 -0.01 -0.06 0.86 0.14 1.0
tired 0.83 0.22 0.10 0.75 0.25 1.2
pain 0.92 0.09 0.02 0.86 0.14 1.0
nauseated 0.67 0.48 0.09 0.68 0.32 1.9
safe 0.70 0.35 0.13 0.63 0.37 1.5
love 0.39 0.79 0.06 0.78 0.22 1.5
recognizing -0.28 0.31 0.70 0.66 0.34 1.7
communicating -0.03 0.21 0.64 0.46 0.54 1.2
guilt 0.27 0.79 0.01 0.70 0.30 1.2
disrespected 0.27 0.77 0.04 0.66 0.34 1.3
free_will 0.71 0.35 0.09 0.64 0.36 1.5
choices 0.24 0.20 0.60 0.45 0.55 1.6
self_restraint 0.24 0.57 0.30 0.47 0.53 1.9
intentions 0.54 0.35 0.26 0.48 0.52 2.2
goal 0.09 0.17 0.62 0.42 0.58 1.2
conscious 0.71 0.36 0.12 0.64 0.36 1.6
self_aware 0.53 0.48 0.21 0.55 0.45 2.3
desires 0.70 0.40 0.11 0.66 0.34 1.6
embarrassed 0.20 0.85 -0.02 0.76 0.24 1.1
emo_recog 0.06 0.72 0.28 0.60 0.40 1.3
joy 0.53 0.68 0.10 0.75 0.25 1.9
morality -0.04 0.62 0.31 0.48 0.52 1.5
personality 0.23 0.67 0.30 0.59 0.41 1.6
pleasure 0.75 0.41 0.11 0.75 0.25 1.6
pride 0.29 0.84 0.05 0.79 0.21 1.3
MR1 MR2 MR3
SS loadings 10.13 9.69 5.12
Proportion Var 0.25 0.24 0.13
Cumulative Var 0.25 0.50 0.62
Proportion Explained 0.41 0.39 0.21
Cumulative Proportion 0.41 0.79 1.00
Mean item complexity = 1.5
Test of the hypothesis that 3 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 36.39 with Chi Square of 14185
The degrees of freedom for the model are 663 and the objective function was 3.83
The root mean square of the residuals (RMSR) is 0.03
The df corrected root mean square of the residuals is 0.03
The harmonic number of observations is 405 with the empirical chi square 479.29 with prob < 1
The total number of observations was 405 with Likelihood Chi Square = 1486.88 with prob < 4.2e-65
Tucker Lewis Index of factoring reliability = 0.927
RMSEA index = 0.058 and the 90 % confidence intervals are 0.052 0.059
BIC = -2493.69
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3
Correlation of scores with factors 0.98 0.97 0.95
Multiple R square of scores with factors 0.97 0.95 0.90
Minimum correlation of possible factor scores 0.93 0.90 0.81Factor loadings table
Study 2
Design: 2 conditions (beetle, robot), between-subjects (replication of Study 1) Date conducted: 2016-01-12
Demographics
Joining, by = c("condition", "min_age", "max_age", "median_age", "mean_age", "sd_age")
Column `condition` joining factor and character vector, coercing into character vectorExploratory factor analysis
Step 1: Run maximal EFA (without and with rotation)
Factor Analysis using method = minres
Call: fa(r = d2_all, nfactors = 13, rotate = "none", fm = "minres",
cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11
happy 0.83 -0.08 -0.18 -0.18 0.15 -0.04 -0.04 0.01 -0.08 0.02 0.00
depressed 0.76 -0.01 -0.37 -0.02 0.13 0.04 -0.01 0.15 0.00 0.02 -0.12
fear 0.76 -0.36 0.21 0.04 0.05 -0.03 -0.06 0.00 0.03 0.00 0.08
angry 0.77 -0.17 -0.06 -0.05 0.19 0.02 -0.06 -0.13 0.13 -0.23 -0.07
calm 0.74 -0.05 0.08 -0.23 0.04 -0.08 -0.03 -0.04 -0.14 0.01 0.03
sounds 0.23 0.46 0.50 -0.11 0.13 0.12 0.02 0.05 0.05 -0.03 0.14
seeing 0.45 0.24 0.55 0.08 -0.05 0.13 -0.12 -0.07 0.01 0.07 0.04
temperature 0.32 0.28 0.53 0.16 0.16 0.02 -0.10 0.11 -0.10 -0.02 -0.08
odors 0.48 0.10 0.51 0.34 0.09 -0.02 -0.16 0.13 -0.16 -0.06 -0.10
depth 0.26 0.47 0.35 0.33 0.25 -0.38 0.34 0.01 0.06 0.02 0.04
computations -0.26 0.83 -0.12 -0.13 0.13 0.02 0.06 0.08 0.02 0.00 -0.01
thoughts 0.77 0.00 -0.01 -0.11 -0.23 -0.10 0.12 0.07 -0.08 0.01 -0.05
reasoning 0.36 0.59 -0.04 0.03 -0.21 -0.08 -0.03 0.00 -0.08 0.06 -0.07
remembering 0.19 0.69 0.24 -0.19 -0.01 0.08 0.00 0.06 -0.01 0.06 0.10
beliefs 0.53 0.26 -0.33 0.07 -0.08 0.20 0.16 0.00 -0.24 0.06 0.01
hungry 0.61 -0.62 0.31 0.19 -0.02 0.08 -0.04 0.00 -0.02 0.07 0.01
tired 0.73 -0.40 0.26 0.02 0.03 0.04 0.04 -0.08 0.06 0.02 0.04
pain 0.67 -0.52 0.28 0.11 -0.01 0.04 -0.02 0.01 0.07 0.09 -0.01
nauseated 0.70 -0.20 -0.02 0.00 0.12 0.03 0.00 -0.02 0.02 0.13 0.05
safe 0.75 -0.22 0.13 -0.05 -0.02 -0.09 -0.03 -0.14 -0.18 -0.06 0.22
love 0.74 0.00 -0.34 -0.05 0.08 0.09 0.05 0.14 0.02 0.06 -0.05
recognizing 0.20 0.74 0.04 -0.08 0.11 0.02 -0.02 0.02 0.00 -0.02 0.09
communicating 0.36 0.51 0.38 -0.05 0.04 0.20 -0.02 0.02 0.12 0.03 -0.02
guilt 0.64 0.14 -0.55 0.15 0.07 0.10 -0.01 0.01 -0.02 -0.02 0.02
disrespected 0.64 0.09 -0.43 0.17 0.04 0.03 -0.07 0.01 0.09 -0.14 0.09
free_will 0.66 -0.28 0.23 -0.01 -0.17 0.10 0.05 0.16 0.19 0.03 -0.03
choices 0.47 0.43 0.35 -0.10 -0.11 0.05 0.03 -0.04 0.08 0.07 -0.01
self_restraint 0.49 0.35 -0.15 0.09 -0.22 -0.05 0.03 -0.03 -0.04 -0.09 -0.07
intentions 0.67 0.00 0.25 0.00 -0.22 0.12 0.25 -0.13 0.04 -0.15 -0.04
goal 0.40 0.44 0.38 -0.05 -0.02 0.09 -0.03 -0.16 -0.08 -0.15 -0.14
conscious 0.72 -0.27 0.21 -0.10 -0.19 -0.11 0.02 0.14 -0.06 -0.04 0.13
self_aware 0.73 0.06 -0.03 -0.08 -0.20 -0.19 -0.07 0.27 0.08 -0.12 0.02
desires 0.79 -0.16 0.03 -0.02 0.02 0.01 0.18 -0.12 0.02 0.09 -0.12
embarrassed 0.61 0.12 -0.53 0.15 0.06 0.16 0.05 0.08 -0.06 -0.01 0.03
emo_recog 0.57 0.42 -0.27 0.14 -0.08 -0.12 -0.11 -0.14 0.07 0.14 0.03
joy 0.82 -0.07 -0.14 -0.22 0.20 -0.11 -0.08 -0.07 -0.01 -0.02 -0.04
morality 0.40 0.52 -0.27 0.19 -0.21 -0.11 -0.22 -0.12 0.09 0.07 0.01
personality 0.64 0.31 -0.16 -0.14 0.03 -0.13 -0.06 -0.05 0.09 0.03 -0.05
pleasure 0.81 -0.24 0.03 -0.17 0.09 -0.09 -0.01 0.00 -0.01 0.11 -0.12
pride 0.70 0.09 -0.47 0.15 0.03 0.13 0.06 -0.01 0.06 -0.04 0.12
MR12 MR13 h2 u2 com
happy -0.02 -0.04 0.80 0.203 1.3
depressed -0.03 0.01 0.76 0.240 1.7
fear 0.05 0.09 0.77 0.226 1.7
angry 0.08 0.16 0.79 0.214 1.8
calm -0.04 0.04 0.65 0.353 1.4
sounds 0.11 -0.08 0.61 0.392 3.2
seeing -0.11 0.05 0.63 0.374 2.9
temperature 0.06 -0.05 0.55 0.448 3.2
odors 0.03 -0.05 0.71 0.294 3.7
depth -0.08 0.01 0.86 0.141 6.2
computations 0.07 -0.02 0.82 0.179 1.4
thoughts 0.05 -0.04 0.71 0.290 1.4
reasoning 0.10 -0.02 0.56 0.441 2.2
remembering -0.05 -0.01 0.63 0.375 1.7
beliefs 0.02 0.16 0.61 0.385 3.7
hungry -0.05 0.00 0.90 0.099 2.8
tired -0.02 0.04 0.78 0.219 1.9
pain 0.01 0.05 0.83 0.169 2.4
nauseated 0.08 0.05 0.58 0.419 1.4
safe -0.02 -0.08 0.75 0.254 1.7
love 0.03 -0.02 0.70 0.299 1.6
recognizing 0.03 0.13 0.63 0.369 1.3
communicating -0.10 0.09 0.61 0.389 3.5
guilt -0.14 0.00 0.80 0.204 2.4
disrespected 0.10 -0.06 0.69 0.309 2.3
free_will -0.06 -0.11 0.69 0.308 2.3
choices 0.04 -0.04 0.56 0.435 3.3
self_restraint -0.09 0.12 0.49 0.513 3.0
intentions -0.04 -0.07 0.68 0.320 2.2
goal -0.02 -0.07 0.59 0.409 4.0
conscious 0.01 0.03 0.74 0.258 2.0
self_aware 0.02 0.08 0.72 0.277 1.8
desires 0.19 -0.01 0.76 0.244 1.5
embarrassed -0.08 -0.08 0.74 0.257 2.5
emo_recog 0.02 0.02 0.68 0.319 3.1
joy -0.08 -0.12 0.84 0.160 1.5
morality 0.06 -0.05 0.67 0.328 4.2
personality -0.16 -0.03 0.61 0.390 2.1
pleasure 0.01 0.02 0.78 0.217 1.4
pride 0.06 -0.08 0.79 0.211 2.2
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11 MR12
SS loadings 14.94 5.37 3.77 0.79 0.68 0.52 0.41 0.36 0.31 0.26 0.25 0.22
Proportion Var 0.37 0.13 0.09 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Var 0.37 0.51 0.60 0.62 0.64 0.65 0.66 0.67 0.68 0.69 0.69 0.70
Proportion Explained 0.53 0.19 0.13 0.03 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Proportion 0.53 0.72 0.86 0.89 0.91 0.93 0.94 0.96 0.97 0.98 0.99 0.99
MR13
SS loadings 0.20
Proportion Var 0.00
Cumulative Var 0.70
Proportion Explained 0.01
Cumulative Proportion 1.00
Mean item complexity = 2.4
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 33.07 with Chi Square of 12924.08
The degrees of freedom for the model are 338 and the objective function was 1.02
The root mean square of the residuals (RMSR) is 0.01
The df corrected root mean square of the residuals is 0.02
The harmonic number of observations is 406 with the empirical chi square 64.06 with prob < 1
The total number of observations was 406 with Likelihood Chi Square = 390.7 with prob < 0.025
Tucker Lewis Index of factoring reliability = 0.99
RMSEA index = 0.024 and the 90 % confidence intervals are 0.008 0.028
BIC = -1639.45
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5 MR6 MR7
Correlation of scores with factors 0.99 0.98 0.96 0.88 0.84 0.82 0.79
Multiple R square of scores with factors 0.98 0.95 0.93 0.77 0.71 0.68 0.62
Minimum correlation of possible factor scores 0.97 0.90 0.86 0.54 0.42 0.36 0.24
MR8 MR9 MR10 MR11 MR12 MR13
Correlation of scores with factors 0.75 0.71 0.69 0.68 0.66 0.64
Multiple R square of scores with factors 0.56 0.50 0.48 0.46 0.44 0.40
Minimum correlation of possible factor scores 0.12 0.00 -0.04 -0.07 -0.12 -0.19
Factor Analysis using method = minres
Call: fa(r = d2_all, nfactors = 13, rotate = "none", fm = "minres",
cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11
happy 0.83 -0.08 -0.18 -0.18 0.15 -0.04 -0.04 0.01 -0.08 0.02 0.00
depressed 0.76 -0.01 -0.37 -0.02 0.13 0.04 -0.01 0.15 0.00 0.02 -0.12
fear 0.76 -0.36 0.21 0.04 0.05 -0.03 -0.06 0.00 0.03 0.00 0.08
angry 0.77 -0.17 -0.06 -0.05 0.19 0.02 -0.06 -0.13 0.13 -0.23 -0.07
calm 0.74 -0.05 0.08 -0.23 0.04 -0.08 -0.03 -0.04 -0.14 0.01 0.03
sounds 0.23 0.46 0.50 -0.11 0.13 0.12 0.02 0.05 0.05 -0.03 0.14
seeing 0.45 0.24 0.55 0.08 -0.05 0.13 -0.12 -0.07 0.01 0.07 0.04
temperature 0.32 0.28 0.53 0.16 0.16 0.02 -0.10 0.11 -0.10 -0.02 -0.08
odors 0.48 0.10 0.51 0.34 0.09 -0.02 -0.16 0.13 -0.16 -0.06 -0.10
depth 0.26 0.47 0.35 0.33 0.25 -0.38 0.34 0.01 0.06 0.02 0.04
computations -0.26 0.83 -0.12 -0.13 0.13 0.02 0.06 0.08 0.02 0.00 -0.01
thoughts 0.77 0.00 -0.01 -0.11 -0.23 -0.10 0.12 0.07 -0.08 0.01 -0.05
reasoning 0.36 0.59 -0.04 0.03 -0.21 -0.08 -0.03 0.00 -0.08 0.06 -0.07
remembering 0.19 0.69 0.24 -0.19 -0.01 0.08 0.00 0.06 -0.01 0.06 0.10
beliefs 0.53 0.26 -0.33 0.07 -0.08 0.20 0.16 0.00 -0.24 0.06 0.01
hungry 0.61 -0.62 0.31 0.19 -0.02 0.08 -0.04 0.00 -0.02 0.07 0.01
tired 0.73 -0.40 0.26 0.02 0.03 0.04 0.04 -0.08 0.06 0.02 0.04
pain 0.67 -0.52 0.28 0.11 -0.01 0.04 -0.02 0.01 0.07 0.09 -0.01
nauseated 0.70 -0.20 -0.02 0.00 0.12 0.03 0.00 -0.02 0.02 0.13 0.05
safe 0.75 -0.22 0.13 -0.05 -0.02 -0.09 -0.03 -0.14 -0.18 -0.06 0.22
love 0.74 0.00 -0.34 -0.05 0.08 0.09 0.05 0.14 0.02 0.06 -0.05
recognizing 0.20 0.74 0.04 -0.08 0.11 0.02 -0.02 0.02 0.00 -0.02 0.09
communicating 0.36 0.51 0.38 -0.05 0.04 0.20 -0.02 0.02 0.12 0.03 -0.02
guilt 0.64 0.14 -0.55 0.15 0.07 0.10 -0.01 0.01 -0.02 -0.02 0.02
disrespected 0.64 0.09 -0.43 0.17 0.04 0.03 -0.07 0.01 0.09 -0.14 0.09
free_will 0.66 -0.28 0.23 -0.01 -0.17 0.10 0.05 0.16 0.19 0.03 -0.03
choices 0.47 0.43 0.35 -0.10 -0.11 0.05 0.03 -0.04 0.08 0.07 -0.01
self_restraint 0.49 0.35 -0.15 0.09 -0.22 -0.05 0.03 -0.03 -0.04 -0.09 -0.07
intentions 0.67 0.00 0.25 0.00 -0.22 0.12 0.25 -0.13 0.04 -0.15 -0.04
goal 0.40 0.44 0.38 -0.05 -0.02 0.09 -0.03 -0.16 -0.08 -0.15 -0.14
conscious 0.72 -0.27 0.21 -0.10 -0.19 -0.11 0.02 0.14 -0.06 -0.04 0.13
self_aware 0.73 0.06 -0.03 -0.08 -0.20 -0.19 -0.07 0.27 0.08 -0.12 0.02
desires 0.79 -0.16 0.03 -0.02 0.02 0.01 0.18 -0.12 0.02 0.09 -0.12
embarrassed 0.61 0.12 -0.53 0.15 0.06 0.16 0.05 0.08 -0.06 -0.01 0.03
emo_recog 0.57 0.42 -0.27 0.14 -0.08 -0.12 -0.11 -0.14 0.07 0.14 0.03
joy 0.82 -0.07 -0.14 -0.22 0.20 -0.11 -0.08 -0.07 -0.01 -0.02 -0.04
morality 0.40 0.52 -0.27 0.19 -0.21 -0.11 -0.22 -0.12 0.09 0.07 0.01
personality 0.64 0.31 -0.16 -0.14 0.03 -0.13 -0.06 -0.05 0.09 0.03 -0.05
pleasure 0.81 -0.24 0.03 -0.17 0.09 -0.09 -0.01 0.00 -0.01 0.11 -0.12
pride 0.70 0.09 -0.47 0.15 0.03 0.13 0.06 -0.01 0.06 -0.04 0.12
MR12 MR13 h2 u2 com
happy -0.02 -0.04 0.80 0.203 1.3
depressed -0.03 0.01 0.76 0.240 1.7
fear 0.05 0.09 0.77 0.226 1.7
angry 0.08 0.16 0.79 0.214 1.8
calm -0.04 0.04 0.65 0.353 1.4
sounds 0.11 -0.08 0.61 0.392 3.2
seeing -0.11 0.05 0.63 0.374 2.9
temperature 0.06 -0.05 0.55 0.448 3.2
odors 0.03 -0.05 0.71 0.294 3.7
depth -0.08 0.01 0.86 0.141 6.2
computations 0.07 -0.02 0.82 0.179 1.4
thoughts 0.05 -0.04 0.71 0.290 1.4
reasoning 0.10 -0.02 0.56 0.441 2.2
remembering -0.05 -0.01 0.63 0.375 1.7
beliefs 0.02 0.16 0.61 0.385 3.7
hungry -0.05 0.00 0.90 0.099 2.8
tired -0.02 0.04 0.78 0.219 1.9
pain 0.01 0.05 0.83 0.169 2.4
nauseated 0.08 0.05 0.58 0.419 1.4
safe -0.02 -0.08 0.75 0.254 1.7
love 0.03 -0.02 0.70 0.299 1.6
recognizing 0.03 0.13 0.63 0.369 1.3
communicating -0.10 0.09 0.61 0.389 3.5
guilt -0.14 0.00 0.80 0.204 2.4
disrespected 0.10 -0.06 0.69 0.309 2.3
free_will -0.06 -0.11 0.69 0.308 2.3
choices 0.04 -0.04 0.56 0.435 3.3
self_restraint -0.09 0.12 0.49 0.513 3.0
intentions -0.04 -0.07 0.68 0.320 2.2
goal -0.02 -0.07 0.59 0.409 4.0
conscious 0.01 0.03 0.74 0.258 2.0
self_aware 0.02 0.08 0.72 0.277 1.8
desires 0.19 -0.01 0.76 0.244 1.5
embarrassed -0.08 -0.08 0.74 0.257 2.5
emo_recog 0.02 0.02 0.68 0.319 3.1
joy -0.08 -0.12 0.84 0.160 1.5
morality 0.06 -0.05 0.67 0.328 4.2
personality -0.16 -0.03 0.61 0.390 2.1
pleasure 0.01 0.02 0.78 0.217 1.4
pride 0.06 -0.08 0.79 0.211 2.2
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11 MR12
SS loadings 14.94 5.37 3.77 0.79 0.68 0.52 0.41 0.36 0.31 0.26 0.25 0.22
Proportion Var 0.37 0.13 0.09 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Var 0.37 0.51 0.60 0.62 0.64 0.65 0.66 0.67 0.68 0.69 0.69 0.70
Proportion Explained 0.53 0.19 0.13 0.03 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Proportion 0.53 0.72 0.86 0.89 0.91 0.93 0.94 0.96 0.97 0.98 0.99 0.99
MR13
SS loadings 0.20
Proportion Var 0.00
Cumulative Var 0.70
Proportion Explained 0.01
Cumulative Proportion 1.00
Mean item complexity = 2.4
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 33.07 with Chi Square of 12924.08
The degrees of freedom for the model are 338 and the objective function was 1.02
The root mean square of the residuals (RMSR) is 0.01
The df corrected root mean square of the residuals is 0.02
The harmonic number of observations is 406 with the empirical chi square 64.06 with prob < 1
The total number of observations was 406 with Likelihood Chi Square = 390.7 with prob < 0.025
Tucker Lewis Index of factoring reliability = 0.99
RMSEA index = 0.024 and the 90 % confidence intervals are 0.008 0.028
BIC = -1639.45
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5 MR6 MR7
Correlation of scores with factors 0.99 0.98 0.96 0.88 0.84 0.82 0.79
Multiple R square of scores with factors 0.98 0.95 0.93 0.77 0.71 0.68 0.62
Minimum correlation of possible factor scores 0.97 0.90 0.86 0.54 0.42 0.36 0.24
MR8 MR9 MR10 MR11 MR12 MR13
Correlation of scores with factors 0.75 0.71 0.69 0.68 0.66 0.64
Multiple R square of scores with factors 0.56 0.50 0.48 0.46 0.44 0.40
Minimum correlation of possible factor scores 0.12 0.00 -0.04 -0.07 -0.12 -0.19
[1] 3[1] 3Step 2: Run EFA with varimax rotation
Factor Analysis using method = minres
Call: fa(r = d2_all, nfactors = nfactors_d2_all, rotate = chosenRotType,
fm = "minres", cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 h2 u2 com
happy 0.57 0.63 0.08 0.73 0.27 2.0
depressed 0.41 0.73 0.00 0.70 0.30 1.6
fear 0.83 0.21 0.09 0.75 0.25 1.2
angry 0.62 0.47 0.08 0.61 0.39 1.9
calm 0.59 0.39 0.24 0.56 0.44 2.1
sounds 0.07 -0.06 0.71 0.51 0.49 1.0
seeing 0.37 -0.02 0.65 0.56 0.44 1.6
temperature 0.25 -0.07 0.61 0.44 0.56 1.4
odors 0.45 -0.01 0.51 0.46 0.54 2.0
depth 0.03 0.08 0.58 0.34 0.66 1.0
computations -0.73 0.19 0.45 0.77 0.23 1.8
thoughts 0.55 0.49 0.23 0.60 0.40 2.3
reasoning -0.11 0.44 0.52 0.48 0.52 2.1
remembering -0.19 0.18 0.70 0.56 0.44 1.3
beliefs 0.11 0.64 0.15 0.44 0.56 1.2
hungry 0.91 -0.04 -0.07 0.84 0.16 1.0
tired 0.86 0.14 0.09 0.77 0.23 1.1
pain 0.90 0.05 0.00 0.81 0.19 1.0
nauseated 0.62 0.39 0.06 0.54 0.46 1.7
safe 0.71 0.31 0.15 0.62 0.38 1.5
love 0.41 0.70 0.02 0.66 0.34 1.6
recognizing -0.28 0.34 0.62 0.59 0.41 2.0
communicating 0.09 0.12 0.71 0.53 0.47 1.1
guilt 0.18 0.84 -0.04 0.73 0.27 1.1
disrespected 0.25 0.73 -0.01 0.60 0.40 1.2
free_will 0.72 0.17 0.14 0.56 0.44 1.2
choices 0.21 0.18 0.67 0.53 0.47 1.3
self_restraint 0.09 0.53 0.31 0.38 0.62 1.7
intentions 0.56 0.25 0.35 0.50 0.50 2.1
goal 0.16 0.13 0.67 0.50 0.50 1.2
conscious 0.75 0.22 0.15 0.63 0.37 1.3
self_aware 0.47 0.49 0.25 0.52 0.48 2.5
desires 0.67 0.42 0.14 0.64 0.36 1.8
embarrassed 0.17 0.79 -0.05 0.66 0.34 1.1
emo_recog 0.06 0.69 0.31 0.57 0.43 1.4
joy 0.58 0.59 0.11 0.69 0.31 2.1
morality -0.11 0.60 0.33 0.47 0.53 1.6
personality 0.21 0.61 0.32 0.52 0.48 1.8
pleasure 0.73 0.41 0.09 0.71 0.29 1.6
pride 0.27 0.80 -0.01 0.71 0.29 1.2
MR1 MR2 MR3
SS loadings 9.98 8.28 5.52
Proportion Var 0.25 0.21 0.14
Cumulative Var 0.25 0.46 0.59
Proportion Explained 0.42 0.35 0.23
Cumulative Proportion 0.42 0.77 1.00
Mean item complexity = 1.5
Test of the hypothesis that 3 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 33.07 with Chi Square of 12924.08
The degrees of freedom for the model are 663 and the objective function was 3.95
The root mean square of the residuals (RMSR) is 0.03
The df corrected root mean square of the residuals is 0.03
The harmonic number of observations is 406 with the empirical chi square 586.55 with prob < 0.98
The total number of observations was 406 with Likelihood Chi Square = 1534.4 with prob < 6.4e-71
Tucker Lewis Index of factoring reliability = 0.915
RMSEA index = 0.059 and the 90 % confidence intervals are 0.053 0.061
BIC = -2447.81
Fit based upon off diagonal values = 0.99
Measures of factor score adequacy
MR1 MR2 MR3
Correlation of scores with factors 0.98 0.97 0.96
Multiple R square of scores with factors 0.96 0.94 0.91
Minimum correlation of possible factor scores 0.93 0.88 0.83Factor loadings table
Study 3
Design: 2 conditions (beetle, robot), within-subjects Date conducted: 2016-01-10
Exploratory factor analysis
Step 1: Run maximal EFA (without and with rotation)
Factor Analysis using method = minres
Call: fa(r = d3_all, nfactors = 13, rotate = "none", fm = "minres",
cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11
happy 0.86 0.00 -0.16 0.06 -0.13 -0.12 -0.03 -0.09 -0.09 0.12 -0.12
depressed 0.75 0.07 -0.33 0.20 -0.08 0.02 -0.05 -0.05 0.08 -0.05 0.07
fear 0.80 -0.33 0.09 -0.04 -0.08 0.01 -0.03 -0.04 0.03 0.06 0.00
angry 0.81 -0.08 -0.15 0.03 -0.16 0.01 0.03 0.02 -0.09 0.08 -0.02
calm 0.81 -0.07 0.04 -0.07 -0.12 -0.08 -0.04 -0.13 0.05 0.03 -0.09
sounds 0.32 0.29 0.57 0.24 0.00 -0.19 0.17 -0.13 -0.18 -0.06 0.00
seeing 0.40 0.11 0.52 0.11 0.07 -0.16 -0.01 -0.07 -0.10 -0.08 0.00
temperature 0.42 0.11 0.59 0.21 0.04 0.07 0.05 0.08 0.07 0.17 0.04
odors 0.53 -0.06 0.45 0.20 0.21 -0.05 -0.05 0.14 0.02 0.07 -0.04
depth 0.39 0.30 0.40 0.17 0.03 0.14 0.15 0.01 0.23 -0.14 -0.04
computations -0.40 0.73 -0.13 -0.03 -0.16 0.03 0.12 0.04 0.10 0.06 -0.02
thoughts 0.81 0.06 0.02 -0.20 -0.05 -0.17 0.11 0.02 0.03 -0.03 0.04
reasoning 0.38 0.63 0.06 -0.14 -0.07 -0.08 -0.04 -0.06 0.09 -0.05 0.11
remembering 0.24 0.60 0.27 0.00 -0.15 0.14 -0.03 0.06 -0.11 -0.02 0.08
beliefs 0.55 0.27 -0.31 0.03 0.23 0.19 0.16 -0.05 -0.09 0.03 -0.07
hungry 0.67 -0.60 0.21 0.07 0.12 0.03 -0.11 -0.03 0.04 -0.01 0.05
tired 0.77 -0.40 0.09 0.04 0.01 0.08 -0.09 -0.06 0.10 0.00 -0.04
pain 0.71 -0.53 0.20 0.05 0.06 0.00 -0.05 0.02 -0.01 -0.06 0.03
nauseated 0.78 -0.22 -0.06 0.12 -0.15 0.08 -0.01 -0.04 0.14 -0.14 0.05
safe 0.75 -0.24 0.10 -0.06 0.00 -0.08 -0.11 -0.02 -0.07 0.05 0.09
love 0.80 0.06 -0.22 0.08 -0.08 0.05 -0.04 -0.03 -0.07 -0.13 0.10
recognizing 0.22 0.71 0.17 0.03 -0.13 -0.18 -0.06 -0.02 0.07 0.00 -0.03
communicating 0.22 0.54 0.26 0.00 -0.01 0.05 -0.18 0.01 0.01 0.03 -0.03
guilt 0.66 0.19 -0.39 0.18 0.06 -0.04 0.02 0.07 0.01 0.07 0.17
disrespected 0.67 0.18 -0.38 0.19 0.07 0.08 0.07 0.01 -0.07 0.00 0.09
free_will 0.79 -0.21 0.07 -0.22 0.10 -0.02 0.11 -0.03 -0.06 -0.08 0.01
choices 0.52 0.29 0.34 -0.25 0.03 0.04 -0.07 -0.04 0.08 0.16 0.09
self_restraint 0.60 0.25 -0.14 -0.14 0.00 0.16 -0.01 -0.20 -0.04 -0.03 0.03
intentions 0.70 -0.02 0.18 -0.28 0.00 0.11 0.05 0.13 -0.06 0.01 0.09
goal 0.43 0.36 0.30 -0.02 -0.10 0.30 -0.11 0.10 -0.15 -0.07 -0.12
conscious 0.80 -0.19 0.08 -0.15 0.06 -0.05 0.10 0.02 0.04 -0.04 0.02
self_aware 0.74 0.00 0.00 -0.19 0.04 -0.12 0.11 0.08 0.08 -0.03 -0.13
desires 0.84 -0.22 0.08 -0.11 -0.06 0.10 0.11 0.12 -0.02 0.03 -0.01
embarrassed 0.63 0.16 -0.42 0.15 0.05 -0.14 0.02 0.17 -0.01 0.05 0.02
emo_recog 0.59 0.40 -0.16 -0.09 0.24 -0.11 -0.13 0.04 0.00 -0.04 -0.01
joy 0.84 -0.01 -0.19 0.10 -0.18 -0.03 0.05 -0.04 0.01 0.12 -0.06
morality 0.40 0.53 -0.15 -0.04 0.37 0.08 0.01 -0.17 0.05 0.08 -0.04
personality 0.68 0.32 -0.09 -0.08 0.00 -0.13 -0.07 0.14 -0.04 -0.12 -0.06
pleasure 0.84 -0.22 -0.02 0.02 -0.12 0.10 0.05 -0.02 0.00 0.06 -0.07
pride 0.71 0.17 -0.39 0.08 0.07 0.01 -0.12 0.07 0.03 -0.07 -0.14
MR12 MR13 h2 u2 com
happy 0.10 -0.06 0.86 0.14 1.3
depressed -0.05 0.00 0.75 0.25 1.7
fear 0.01 0.06 0.77 0.23 1.4
angry -0.01 0.02 0.73 0.27 1.2
calm -0.06 0.01 0.72 0.28 1.2
sounds 0.02 0.06 0.70 0.30 3.7
seeing -0.04 0.04 0.50 0.50 2.6
temperature 0.10 0.07 0.65 0.35 2.7
odors -0.01 -0.03 0.60 0.40 2.9
depth -0.01 -0.06 0.56 0.44 4.9
computations 0.15 0.07 0.80 0.20 2.0
thoughts -0.03 -0.03 0.76 0.24 1.3
reasoning 0.04 -0.03 0.60 0.40 2.0
remembering -0.07 -0.03 0.56 0.44 2.2
beliefs 0.00 0.02 0.60 0.40 3.2
hungry -0.01 -0.05 0.90 0.10 2.4
tired 0.06 0.05 0.81 0.19 1.7
pain 0.00 -0.05 0.84 0.16 2.1
nauseated 0.04 0.07 0.76 0.24 1.5
safe -0.03 0.06 0.68 0.32 1.4
love 0.14 -0.02 0.77 0.23 1.4
recognizing -0.10 -0.03 0.66 0.34 1.6
communicating -0.03 -0.01 0.44 0.56 2.2
guilt -0.04 -0.07 0.71 0.29 2.3
disrespected -0.14 0.06 0.71 0.29 2.3
free_will 0.12 0.09 0.78 0.22 1.5
choices -0.01 0.12 0.59 0.41 3.6
self_restraint -0.04 0.02 0.53 0.47 2.1
intentions -0.02 0.02 0.64 0.36 1.7
goal 0.01 -0.05 0.57 0.43 4.7
conscious 0.03 -0.11 0.75 0.25 1.3
self_aware -0.16 0.04 0.67 0.33 1.5
desires 0.01 -0.03 0.82 0.18 1.3
embarrassed 0.00 0.04 0.68 0.32 2.4
emo_recog 0.09 -0.08 0.66 0.34 2.8
joy 0.07 -0.11 0.83 0.17 1.3
morality 0.01 -0.06 0.66 0.34 3.4
personality 0.06 -0.01 0.64 0.36 1.8
pleasure -0.10 -0.06 0.81 0.19 1.3
pride 0.00 0.20 0.77 0.23 2.2
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11 MR12
SS loadings 16.94 4.54 2.90 0.73 0.58 0.49 0.31 0.28 0.27 0.23 0.20 0.19
Proportion Var 0.42 0.11 0.07 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00
Cumulative Var 0.42 0.54 0.61 0.63 0.64 0.65 0.66 0.67 0.68 0.68 0.69 0.69
Proportion Explained 0.61 0.16 0.10 0.03 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Proportion 0.61 0.77 0.88 0.90 0.92 0.94 0.95 0.96 0.97 0.98 0.99 0.99
MR13
SS loadings 0.17
Proportion Var 0.00
Cumulative Var 0.70
Proportion Explained 0.01
Cumulative Proportion 1.00
Mean item complexity = 2.2
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 34.22 with Chi Square of 13170.85
The degrees of freedom for the model are 338 and the objective function was 1.01
The root mean square of the residuals (RMSR) is 0.01
The df corrected root mean square of the residuals is 0.02
The harmonic number of observations is 400 with the empirical chi square 61.88 with prob < 1
The total number of observations was 400 with Likelihood Chi Square = 381 with prob < 0.053
Tucker Lewis Index of factoring reliability = 0.992
RMSEA index = 0.022 and the 90 % confidence intervals are 0 0.027
BIC = -1644.12
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5 MR6 MR7
Correlation of scores with factors 0.99 0.97 0.95 0.84 0.82 0.77 0.72
Multiple R square of scores with factors 0.99 0.94 0.90 0.71 0.67 0.59 0.51
Minimum correlation of possible factor scores 0.97 0.88 0.80 0.41 0.34 0.18 0.02
MR8 MR9 MR10 MR11 MR12 MR13
Correlation of scores with factors 0.68 0.68 0.67 0.65 0.65 0.63
Multiple R square of scores with factors 0.47 0.46 0.45 0.42 0.43 0.40
Minimum correlation of possible factor scores -0.06 -0.07 -0.10 -0.16 -0.14 -0.20
Factor Analysis using method = minres
Call: fa(r = d3_all, nfactors = 13, rotate = "none", fm = "minres",
cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11
happy 0.86 0.00 -0.16 0.06 -0.13 -0.12 -0.03 -0.09 -0.09 0.12 -0.12
depressed 0.75 0.07 -0.33 0.20 -0.08 0.02 -0.05 -0.05 0.08 -0.05 0.07
fear 0.80 -0.33 0.09 -0.04 -0.08 0.01 -0.03 -0.04 0.03 0.06 0.00
angry 0.81 -0.08 -0.15 0.03 -0.16 0.01 0.03 0.02 -0.09 0.08 -0.02
calm 0.81 -0.07 0.04 -0.07 -0.12 -0.08 -0.04 -0.13 0.05 0.03 -0.09
sounds 0.32 0.29 0.57 0.24 0.00 -0.19 0.17 -0.13 -0.18 -0.06 0.00
seeing 0.40 0.11 0.52 0.11 0.07 -0.16 -0.01 -0.07 -0.10 -0.08 0.00
temperature 0.42 0.11 0.59 0.21 0.04 0.07 0.05 0.08 0.07 0.17 0.04
odors 0.53 -0.06 0.45 0.20 0.21 -0.05 -0.05 0.14 0.02 0.07 -0.04
depth 0.39 0.30 0.40 0.17 0.03 0.14 0.15 0.01 0.23 -0.14 -0.04
computations -0.40 0.73 -0.13 -0.03 -0.16 0.03 0.12 0.04 0.10 0.06 -0.02
thoughts 0.81 0.06 0.02 -0.20 -0.05 -0.17 0.11 0.02 0.03 -0.03 0.04
reasoning 0.38 0.63 0.06 -0.14 -0.07 -0.08 -0.04 -0.06 0.09 -0.05 0.11
remembering 0.24 0.60 0.27 0.00 -0.15 0.14 -0.03 0.06 -0.11 -0.02 0.08
beliefs 0.55 0.27 -0.31 0.03 0.23 0.19 0.16 -0.05 -0.09 0.03 -0.07
hungry 0.67 -0.60 0.21 0.07 0.12 0.03 -0.11 -0.03 0.04 -0.01 0.05
tired 0.77 -0.40 0.09 0.04 0.01 0.08 -0.09 -0.06 0.10 0.00 -0.04
pain 0.71 -0.53 0.20 0.05 0.06 0.00 -0.05 0.02 -0.01 -0.06 0.03
nauseated 0.78 -0.22 -0.06 0.12 -0.15 0.08 -0.01 -0.04 0.14 -0.14 0.05
safe 0.75 -0.24 0.10 -0.06 0.00 -0.08 -0.11 -0.02 -0.07 0.05 0.09
love 0.80 0.06 -0.22 0.08 -0.08 0.05 -0.04 -0.03 -0.07 -0.13 0.10
recognizing 0.22 0.71 0.17 0.03 -0.13 -0.18 -0.06 -0.02 0.07 0.00 -0.03
communicating 0.22 0.54 0.26 0.00 -0.01 0.05 -0.18 0.01 0.01 0.03 -0.03
guilt 0.66 0.19 -0.39 0.18 0.06 -0.04 0.02 0.07 0.01 0.07 0.17
disrespected 0.67 0.18 -0.38 0.19 0.07 0.08 0.07 0.01 -0.07 0.00 0.09
free_will 0.79 -0.21 0.07 -0.22 0.10 -0.02 0.11 -0.03 -0.06 -0.08 0.01
choices 0.52 0.29 0.34 -0.25 0.03 0.04 -0.07 -0.04 0.08 0.16 0.09
self_restraint 0.60 0.25 -0.14 -0.14 0.00 0.16 -0.01 -0.20 -0.04 -0.03 0.03
intentions 0.70 -0.02 0.18 -0.28 0.00 0.11 0.05 0.13 -0.06 0.01 0.09
goal 0.43 0.36 0.30 -0.02 -0.10 0.30 -0.11 0.10 -0.15 -0.07 -0.12
conscious 0.80 -0.19 0.08 -0.15 0.06 -0.05 0.10 0.02 0.04 -0.04 0.02
self_aware 0.74 0.00 0.00 -0.19 0.04 -0.12 0.11 0.08 0.08 -0.03 -0.13
desires 0.84 -0.22 0.08 -0.11 -0.06 0.10 0.11 0.12 -0.02 0.03 -0.01
embarrassed 0.63 0.16 -0.42 0.15 0.05 -0.14 0.02 0.17 -0.01 0.05 0.02
emo_recog 0.59 0.40 -0.16 -0.09 0.24 -0.11 -0.13 0.04 0.00 -0.04 -0.01
joy 0.84 -0.01 -0.19 0.10 -0.18 -0.03 0.05 -0.04 0.01 0.12 -0.06
morality 0.40 0.53 -0.15 -0.04 0.37 0.08 0.01 -0.17 0.05 0.08 -0.04
personality 0.68 0.32 -0.09 -0.08 0.00 -0.13 -0.07 0.14 -0.04 -0.12 -0.06
pleasure 0.84 -0.22 -0.02 0.02 -0.12 0.10 0.05 -0.02 0.00 0.06 -0.07
pride 0.71 0.17 -0.39 0.08 0.07 0.01 -0.12 0.07 0.03 -0.07 -0.14
MR12 MR13 h2 u2 com
happy 0.10 -0.06 0.86 0.14 1.3
depressed -0.05 0.00 0.75 0.25 1.7
fear 0.01 0.06 0.77 0.23 1.4
angry -0.01 0.02 0.73 0.27 1.2
calm -0.06 0.01 0.72 0.28 1.2
sounds 0.02 0.06 0.70 0.30 3.7
seeing -0.04 0.04 0.50 0.50 2.6
temperature 0.10 0.07 0.65 0.35 2.7
odors -0.01 -0.03 0.60 0.40 2.9
depth -0.01 -0.06 0.56 0.44 4.9
computations 0.15 0.07 0.80 0.20 2.0
thoughts -0.03 -0.03 0.76 0.24 1.3
reasoning 0.04 -0.03 0.60 0.40 2.0
remembering -0.07 -0.03 0.56 0.44 2.2
beliefs 0.00 0.02 0.60 0.40 3.2
hungry -0.01 -0.05 0.90 0.10 2.4
tired 0.06 0.05 0.81 0.19 1.7
pain 0.00 -0.05 0.84 0.16 2.1
nauseated 0.04 0.07 0.76 0.24 1.5
safe -0.03 0.06 0.68 0.32 1.4
love 0.14 -0.02 0.77 0.23 1.4
recognizing -0.10 -0.03 0.66 0.34 1.6
communicating -0.03 -0.01 0.44 0.56 2.2
guilt -0.04 -0.07 0.71 0.29 2.3
disrespected -0.14 0.06 0.71 0.29 2.3
free_will 0.12 0.09 0.78 0.22 1.5
choices -0.01 0.12 0.59 0.41 3.6
self_restraint -0.04 0.02 0.53 0.47 2.1
intentions -0.02 0.02 0.64 0.36 1.7
goal 0.01 -0.05 0.57 0.43 4.7
conscious 0.03 -0.11 0.75 0.25 1.3
self_aware -0.16 0.04 0.67 0.33 1.5
desires 0.01 -0.03 0.82 0.18 1.3
embarrassed 0.00 0.04 0.68 0.32 2.4
emo_recog 0.09 -0.08 0.66 0.34 2.8
joy 0.07 -0.11 0.83 0.17 1.3
morality 0.01 -0.06 0.66 0.34 3.4
personality 0.06 -0.01 0.64 0.36 1.8
pleasure -0.10 -0.06 0.81 0.19 1.3
pride 0.00 0.20 0.77 0.23 2.2
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11 MR12
SS loadings 16.94 4.54 2.90 0.73 0.58 0.49 0.31 0.28 0.27 0.23 0.20 0.19
Proportion Var 0.42 0.11 0.07 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00
Cumulative Var 0.42 0.54 0.61 0.63 0.64 0.65 0.66 0.67 0.68 0.68 0.69 0.69
Proportion Explained 0.61 0.16 0.10 0.03 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Proportion 0.61 0.77 0.88 0.90 0.92 0.94 0.95 0.96 0.97 0.98 0.99 0.99
MR13
SS loadings 0.17
Proportion Var 0.00
Cumulative Var 0.70
Proportion Explained 0.01
Cumulative Proportion 1.00
Mean item complexity = 2.2
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 34.22 with Chi Square of 13170.85
The degrees of freedom for the model are 338 and the objective function was 1.01
The root mean square of the residuals (RMSR) is 0.01
The df corrected root mean square of the residuals is 0.02
The harmonic number of observations is 400 with the empirical chi square 61.88 with prob < 1
The total number of observations was 400 with Likelihood Chi Square = 381 with prob < 0.053
Tucker Lewis Index of factoring reliability = 0.992
RMSEA index = 0.022 and the 90 % confidence intervals are 0 0.027
BIC = -1644.12
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5 MR6 MR7
Correlation of scores with factors 0.99 0.97 0.95 0.84 0.82 0.77 0.72
Multiple R square of scores with factors 0.99 0.94 0.90 0.71 0.67 0.59 0.51
Minimum correlation of possible factor scores 0.97 0.88 0.80 0.41 0.34 0.18 0.02
MR8 MR9 MR10 MR11 MR12 MR13
Correlation of scores with factors 0.68 0.68 0.67 0.65 0.65 0.63
Multiple R square of scores with factors 0.47 0.46 0.45 0.42 0.43 0.40
Minimum correlation of possible factor scores -0.06 -0.07 -0.10 -0.16 -0.14 -0.20
[1] 3[1] 3Step 2: Run EFA with varimax rotation
Factor Analysis using method = minres
Call: fa(r = d3_all, nfactors = nfactors_d3_all, rotate = chosenRotType,
fm = "minres", cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR3 MR1 MR2 h2 u2 com
happy 0.54 0.66 0.17 0.76 0.24 2.1
depressed 0.37 0.73 0.06 0.68 0.32 1.5
fear 0.79 0.34 0.10 0.76 0.24 1.4
angry 0.56 0.60 0.11 0.69 0.31 2.1
calm 0.61 0.48 0.24 0.66 0.34 2.2
sounds 0.20 -0.04 0.65 0.47 0.53 1.2
seeing 0.36 -0.04 0.55 0.44 0.56 1.7
temperature 0.39 -0.07 0.60 0.52 0.48 1.8
odors 0.53 0.03 0.43 0.47 0.53 1.9
depth 0.19 0.11 0.59 0.39 0.61 1.3
computations -0.79 0.10 0.26 0.70 0.30 1.2
thoughts 0.52 0.55 0.31 0.66 0.34 2.6
reasoning -0.13 0.43 0.57 0.54 0.46 2.0
remembering -0.15 0.20 0.65 0.49 0.51 1.3
beliefs 0.11 0.65 0.14 0.46 0.54 1.2
hungry 0.92 0.08 -0.03 0.86 0.14 1.0
tired 0.83 0.30 0.05 0.77 0.23 1.3
pain 0.90 0.14 0.02 0.83 0.17 1.0
nauseated 0.66 0.47 0.07 0.66 0.34 1.8
safe 0.70 0.34 0.16 0.63 0.37 1.6
love 0.44 0.69 0.15 0.69 0.31 1.8
recognizing -0.26 0.30 0.65 0.58 0.42 1.8
communicating -0.12 0.17 0.60 0.41 0.59 1.3
guilt 0.21 0.76 0.07 0.63 0.37 1.2
disrespected 0.23 0.75 0.08 0.62 0.38 1.2
free_will 0.70 0.39 0.17 0.67 0.33 1.7
choices 0.27 0.23 0.58 0.46 0.54 1.8
self_restraint 0.20 0.57 0.26 0.44 0.56 1.7
intentions 0.54 0.33 0.33 0.52 0.48 2.4
goal 0.15 0.22 0.57 0.39 0.61 1.5
conscious 0.70 0.40 0.19 0.69 0.31 1.8
self_aware 0.50 0.48 0.24 0.54 0.46 2.4
desires 0.74 0.42 0.18 0.76 0.24 1.7
embarrassed 0.19 0.74 0.02 0.59 0.41 1.1
emo_recog 0.10 0.64 0.34 0.53 0.47 1.6
joy 0.52 0.67 0.14 0.74 0.26 2.0
morality -0.11 0.54 0.37 0.44 0.56 1.8
personality 0.23 0.62 0.36 0.57 0.43 1.9
pleasure 0.71 0.48 0.11 0.76 0.24 1.8
pride 0.25 0.77 0.07 0.67 0.33 1.2
MR3 MR1 MR2
SS loadings 10.13 9.03 4.92
Proportion Var 0.25 0.23 0.12
Cumulative Var 0.25 0.48 0.60
Proportion Explained 0.42 0.38 0.20
Cumulative Proportion 0.42 0.80 1.00
Mean item complexity = 1.6
Test of the hypothesis that 3 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 34.22 with Chi Square of 13170.85
The degrees of freedom for the model are 663 and the objective function was 3.61
The root mean square of the residuals (RMSR) is 0.03
The df corrected root mean square of the residuals is 0.03
The harmonic number of observations is 400 with the empirical chi square 508.77 with prob < 1
The total number of observations was 400 with Likelihood Chi Square = 1380.26 with prob < 1.3e-52
Tucker Lewis Index of factoring reliability = 0.932
RMSEA index = 0.054 and the 90 % confidence intervals are 0.048 0.056
BIC = -2592.08
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR3 MR1 MR2
Correlation of scores with factors 0.98 0.96 0.94
Multiple R square of scores with factors 0.96 0.93 0.88
Minimum correlation of possible factor scores 0.92 0.86 0.76Factor loadings table
Study 4
Design: 21 conditions, between-subjects Date conducted: 2016-01-14
Demographics
Joining, by = c("condition", "min_age", "max_age", "median_age", "mean_age", "sd_age")
Column `condition` joining factor and character vector, coercing into character vectorExploratory factor analysis
Step 1: Run maximal EFA (without and with rotation)
Factor Analysis using method = minres
Call: fa(r = d4_all, nfactors = 13, rotate = "none", fm = "minres",
cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11
happy 0.88 -0.07 -0.25 0.15 -0.15 0.05 -0.03 0.07 -0.03 0.09 0.05
depressed 0.81 0.17 -0.16 0.15 0.03 -0.24 0.00 -0.06 0.02 -0.07 -0.01
fear 0.84 -0.37 -0.13 -0.07 0.10 -0.01 0.00 0.04 -0.05 -0.01 0.00
angry 0.86 -0.07 -0.15 0.12 -0.04 -0.09 -0.06 -0.05 0.06 -0.07 -0.04
calm 0.85 -0.20 -0.15 -0.03 -0.02 0.03 -0.02 0.11 -0.08 0.09 0.00
sounds 0.71 -0.32 0.41 0.14 0.13 0.13 0.00 0.03 0.03 0.01 0.02
seeing 0.77 -0.33 0.28 0.07 0.12 0.04 -0.05 -0.01 0.03 -0.01 -0.01
temperature 0.65 -0.31 0.41 0.05 0.18 0.05 0.03 0.04 -0.01 0.02 -0.07
odors 0.79 -0.25 0.17 0.03 0.16 -0.04 0.05 -0.15 0.05 -0.02 -0.08
depth 0.71 -0.03 0.39 -0.11 0.03 -0.04 -0.10 0.03 0.11 0.08 -0.05
computations 0.22 0.57 0.46 0.16 0.03 0.06 -0.06 0.06 0.05 0.09 0.05
thoughts 0.85 0.00 -0.10 -0.08 -0.14 0.14 0.21 -0.21 0.05 -0.03 0.01
reasoning 0.73 0.36 0.22 -0.13 -0.10 0.01 0.07 -0.10 0.01 0.05 -0.01
remembering 0.75 0.06 0.42 0.12 -0.12 -0.10 -0.06 -0.08 0.04 0.01 0.02
beliefs 0.66 0.54 -0.11 -0.12 0.07 0.16 0.07 0.00 0.05 0.07 -0.02
hungry 0.78 -0.44 -0.15 -0.13 0.16 0.03 -0.02 -0.02 -0.02 -0.02 0.06
tired 0.85 -0.37 -0.15 0.03 0.06 -0.03 0.00 -0.02 -0.04 0.01 0.03
pain 0.81 -0.43 -0.17 -0.02 0.19 0.03 0.01 -0.08 -0.03 -0.05 0.07
nauseated 0.82 -0.12 -0.18 0.10 0.03 -0.13 -0.02 -0.04 0.05 0.10 -0.03
safe 0.85 -0.22 -0.11 -0.08 -0.04 0.02 0.03 0.21 -0.06 0.04 0.00
love 0.84 0.05 -0.26 0.15 -0.12 0.09 -0.03 0.02 0.00 -0.03 -0.01
recognizing 0.79 -0.04 0.24 0.33 -0.09 0.05 -0.01 -0.02 -0.01 0.02 0.09
communicating 0.75 -0.08 0.40 0.13 -0.15 0.03 0.12 0.12 -0.13 -0.18 -0.01
guilt 0.74 0.47 -0.18 0.09 0.14 -0.03 0.04 0.00 0.05 0.01 0.09
disrespected 0.73 0.43 -0.12 0.02 0.18 -0.04 0.01 0.07 -0.05 -0.11 0.01
free_will 0.82 -0.07 -0.04 -0.28 -0.07 0.02 -0.14 -0.02 0.04 -0.06 -0.04
choices 0.79 -0.03 0.33 -0.16 -0.07 -0.07 -0.08 0.01 -0.04 0.00 0.00
self_restraint 0.75 0.38 0.10 -0.09 0.05 -0.21 0.00 -0.06 -0.23 0.08 0.10
intentions 0.81 0.03 0.06 -0.26 -0.04 -0.07 0.00 0.11 0.13 0.01 -0.01
goal 0.67 0.10 0.32 -0.24 -0.09 -0.11 0.21 0.05 0.01 -0.05 0.05
conscious 0.82 -0.15 -0.06 -0.13 -0.03 0.10 0.08 -0.01 -0.04 0.02 0.06
self_aware 0.77 0.21 0.02 -0.24 -0.07 0.13 -0.23 -0.08 -0.01 -0.07 0.15
desires 0.87 -0.10 -0.12 -0.09 -0.08 -0.08 0.09 0.06 0.06 0.05 -0.10
embarrassed 0.70 0.50 -0.20 0.08 0.21 0.02 0.09 0.11 0.15 -0.03 0.09
emo_recog 0.77 0.34 -0.02 0.09 0.02 0.01 -0.09 -0.01 -0.04 -0.12 -0.05
joy 0.89 -0.04 -0.23 0.14 -0.15 0.02 0.02 -0.01 -0.03 0.10 0.00
morality 0.70 0.53 -0.01 -0.02 0.12 0.11 0.00 -0.08 -0.19 0.07 -0.19
personality 0.89 0.00 -0.12 0.14 -0.17 0.04 -0.02 -0.02 0.04 -0.07 -0.05
pleasure 0.90 -0.22 -0.15 0.03 -0.04 -0.04 -0.03 -0.03 0.05 0.07 0.01
pride 0.81 0.32 -0.16 -0.03 -0.03 0.01 -0.08 0.08 0.03 -0.08 -0.12
MR12 MR13 h2 u2 com
happy 0.01 -0.05 0.91 0.090 1.4
depressed 0.07 0.11 0.82 0.183 1.5
fear -0.03 0.06 0.88 0.117 1.5
angry -0.08 -0.08 0.82 0.181 1.2
calm 0.02 0.08 0.83 0.175 1.3
sounds -0.04 0.04 0.83 0.170 2.4
seeing -0.09 -0.06 0.82 0.183 1.8
temperature 0.18 -0.03 0.77 0.229 2.7
odors 0.00 -0.03 0.79 0.213 1.5
depth 0.11 -0.02 0.71 0.290 1.9
computations 0.02 0.05 0.64 0.360 2.6
thoughts 0.03 0.13 0.88 0.116 1.5
reasoning -0.05 -0.08 0.76 0.241 1.9
remembering -0.07 0.14 0.82 0.180 1.9
beliefs 0.02 0.01 0.81 0.193 2.3
hungry -0.05 0.00 0.88 0.120 1.9
tired -0.01 -0.04 0.90 0.104 1.5
pain 0.01 0.01 0.92 0.078 1.8
nauseated 0.08 -0.01 0.77 0.233 1.3
safe 0.00 0.13 0.86 0.141 1.4
love 0.06 -0.08 0.83 0.167 1.4
recognizing -0.05 -0.05 0.82 0.180 1.7
communicating 0.00 -0.02 0.85 0.153 2.1
guilt -0.04 0.01 0.84 0.163 2.0
disrespected -0.04 -0.06 0.80 0.204 2.0
free_will -0.03 0.07 0.79 0.206 1.4
choices -0.12 0.02 0.79 0.213 1.6
self_restraint 0.06 -0.01 0.85 0.153 2.1
intentions -0.07 -0.04 0.77 0.230 1.4
goal 0.04 -0.07 0.70 0.297 2.3
conscious 0.04 -0.05 0.73 0.266 1.2
self_aware 0.08 -0.04 0.82 0.185 1.8
desires -0.01 -0.07 0.84 0.163 1.2
embarrassed -0.04 0.03 0.89 0.109 2.5
emo_recog 0.07 0.00 0.75 0.250 1.6
joy -0.05 -0.03 0.90 0.103 1.3
morality -0.09 0.01 0.88 0.117 2.5
personality 0.06 0.03 0.86 0.135 1.2
pleasure -0.01 -0.02 0.89 0.113 1.2
pride 0.02 0.00 0.81 0.189 1.5
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11 MR12
SS loadings 24.49 3.38 2.06 0.74 0.48 0.31 0.27 0.24 0.21 0.17 0.16 0.15
Proportion Var 0.61 0.08 0.05 0.02 0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.00
Cumulative Var 0.61 0.70 0.75 0.77 0.78 0.79 0.79 0.80 0.80 0.81 0.81 0.82
Proportion Explained 0.75 0.10 0.06 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00
Cumulative Proportion 0.75 0.85 0.91 0.93 0.95 0.96 0.97 0.97 0.98 0.99 0.99 1.00
MR13
SS loadings 0.14
Proportion Var 0.00
Cumulative Var 0.82
Proportion Explained 0.00
Cumulative Proportion 1.00
Mean item complexity = 1.7
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 52.07 with Chi Square of 21651.8
The degrees of freedom for the model are 338 and the objective function was 1.29
The root mean square of the residuals (RMSR) is 0.01
The df corrected root mean square of the residuals is 0.01
The harmonic number of observations is 431 with the empirical chi square 34.49 with prob < 1
The total number of observations was 431 with Likelihood Chi Square = 526.8 with prob < 2e-10
Tucker Lewis Index of factoring reliability = 0.979
RMSEA index = 0.039 and the 90 % confidence intervals are 0.03 0.042
BIC = -1523.55
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5 MR6 MR7
Correlation of scores with factors 1.00 0.98 0.96 0.89 0.88 0.80 0.77
Multiple R square of scores with factors 0.99 0.96 0.92 0.80 0.77 0.64 0.60
Minimum correlation of possible factor scores 0.99 0.91 0.83 0.60 0.55 0.28 0.19
MR8 MR9 MR10 MR11 MR12 MR13
Correlation of scores with factors 0.79 0.76 0.71 0.72 0.66 0.67
Multiple R square of scores with factors 0.62 0.59 0.51 0.51 0.44 0.45
Minimum correlation of possible factor scores 0.24 0.17 0.01 0.03 -0.12 -0.10
Factor Analysis using method = minres
Call: fa(r = d4_all, nfactors = 13, rotate = "none", fm = "minres",
cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11
happy 0.88 -0.07 -0.25 0.15 -0.15 0.05 -0.03 0.07 -0.03 0.09 0.05
depressed 0.81 0.17 -0.16 0.15 0.03 -0.24 0.00 -0.06 0.02 -0.07 -0.01
fear 0.84 -0.37 -0.13 -0.07 0.10 -0.01 0.00 0.04 -0.05 -0.01 0.00
angry 0.86 -0.07 -0.15 0.12 -0.04 -0.09 -0.06 -0.05 0.06 -0.07 -0.04
calm 0.85 -0.20 -0.15 -0.03 -0.02 0.03 -0.02 0.11 -0.08 0.09 0.00
sounds 0.71 -0.32 0.41 0.14 0.13 0.13 0.00 0.03 0.03 0.01 0.02
seeing 0.77 -0.33 0.28 0.07 0.12 0.04 -0.05 -0.01 0.03 -0.01 -0.01
temperature 0.65 -0.31 0.41 0.05 0.18 0.05 0.03 0.04 -0.01 0.02 -0.07
odors 0.79 -0.25 0.17 0.03 0.16 -0.04 0.05 -0.15 0.05 -0.02 -0.08
depth 0.71 -0.03 0.39 -0.11 0.03 -0.04 -0.10 0.03 0.11 0.08 -0.05
computations 0.22 0.57 0.46 0.16 0.03 0.06 -0.06 0.06 0.05 0.09 0.05
thoughts 0.85 0.00 -0.10 -0.08 -0.14 0.14 0.21 -0.21 0.05 -0.03 0.01
reasoning 0.73 0.36 0.22 -0.13 -0.10 0.01 0.07 -0.10 0.01 0.05 -0.01
remembering 0.75 0.06 0.42 0.12 -0.12 -0.10 -0.06 -0.08 0.04 0.01 0.02
beliefs 0.66 0.54 -0.11 -0.12 0.07 0.16 0.07 0.00 0.05 0.07 -0.02
hungry 0.78 -0.44 -0.15 -0.13 0.16 0.03 -0.02 -0.02 -0.02 -0.02 0.06
tired 0.85 -0.37 -0.15 0.03 0.06 -0.03 0.00 -0.02 -0.04 0.01 0.03
pain 0.81 -0.43 -0.17 -0.02 0.19 0.03 0.01 -0.08 -0.03 -0.05 0.07
nauseated 0.82 -0.12 -0.18 0.10 0.03 -0.13 -0.02 -0.04 0.05 0.10 -0.03
safe 0.85 -0.22 -0.11 -0.08 -0.04 0.02 0.03 0.21 -0.06 0.04 0.00
love 0.84 0.05 -0.26 0.15 -0.12 0.09 -0.03 0.02 0.00 -0.03 -0.01
recognizing 0.79 -0.04 0.24 0.33 -0.09 0.05 -0.01 -0.02 -0.01 0.02 0.09
communicating 0.75 -0.08 0.40 0.13 -0.15 0.03 0.12 0.12 -0.13 -0.18 -0.01
guilt 0.74 0.47 -0.18 0.09 0.14 -0.03 0.04 0.00 0.05 0.01 0.09
disrespected 0.73 0.43 -0.12 0.02 0.18 -0.04 0.01 0.07 -0.05 -0.11 0.01
free_will 0.82 -0.07 -0.04 -0.28 -0.07 0.02 -0.14 -0.02 0.04 -0.06 -0.04
choices 0.79 -0.03 0.33 -0.16 -0.07 -0.07 -0.08 0.01 -0.04 0.00 0.00
self_restraint 0.75 0.38 0.10 -0.09 0.05 -0.21 0.00 -0.06 -0.23 0.08 0.10
intentions 0.81 0.03 0.06 -0.26 -0.04 -0.07 0.00 0.11 0.13 0.01 -0.01
goal 0.67 0.10 0.32 -0.24 -0.09 -0.11 0.21 0.05 0.01 -0.05 0.05
conscious 0.82 -0.15 -0.06 -0.13 -0.03 0.10 0.08 -0.01 -0.04 0.02 0.06
self_aware 0.77 0.21 0.02 -0.24 -0.07 0.13 -0.23 -0.08 -0.01 -0.07 0.15
desires 0.87 -0.10 -0.12 -0.09 -0.08 -0.08 0.09 0.06 0.06 0.05 -0.10
embarrassed 0.70 0.50 -0.20 0.08 0.21 0.02 0.09 0.11 0.15 -0.03 0.09
emo_recog 0.77 0.34 -0.02 0.09 0.02 0.01 -0.09 -0.01 -0.04 -0.12 -0.05
joy 0.89 -0.04 -0.23 0.14 -0.15 0.02 0.02 -0.01 -0.03 0.10 0.00
morality 0.70 0.53 -0.01 -0.02 0.12 0.11 0.00 -0.08 -0.19 0.07 -0.19
personality 0.89 0.00 -0.12 0.14 -0.17 0.04 -0.02 -0.02 0.04 -0.07 -0.05
pleasure 0.90 -0.22 -0.15 0.03 -0.04 -0.04 -0.03 -0.03 0.05 0.07 0.01
pride 0.81 0.32 -0.16 -0.03 -0.03 0.01 -0.08 0.08 0.03 -0.08 -0.12
MR12 MR13 h2 u2 com
happy 0.01 -0.05 0.91 0.090 1.4
depressed 0.07 0.11 0.82 0.183 1.5
fear -0.03 0.06 0.88 0.117 1.5
angry -0.08 -0.08 0.82 0.181 1.2
calm 0.02 0.08 0.83 0.175 1.3
sounds -0.04 0.04 0.83 0.170 2.4
seeing -0.09 -0.06 0.82 0.183 1.8
temperature 0.18 -0.03 0.77 0.229 2.7
odors 0.00 -0.03 0.79 0.213 1.5
depth 0.11 -0.02 0.71 0.290 1.9
computations 0.02 0.05 0.64 0.360 2.6
thoughts 0.03 0.13 0.88 0.116 1.5
reasoning -0.05 -0.08 0.76 0.241 1.9
remembering -0.07 0.14 0.82 0.180 1.9
beliefs 0.02 0.01 0.81 0.193 2.3
hungry -0.05 0.00 0.88 0.120 1.9
tired -0.01 -0.04 0.90 0.104 1.5
pain 0.01 0.01 0.92 0.078 1.8
nauseated 0.08 -0.01 0.77 0.233 1.3
safe 0.00 0.13 0.86 0.141 1.4
love 0.06 -0.08 0.83 0.167 1.4
recognizing -0.05 -0.05 0.82 0.180 1.7
communicating 0.00 -0.02 0.85 0.153 2.1
guilt -0.04 0.01 0.84 0.163 2.0
disrespected -0.04 -0.06 0.80 0.204 2.0
free_will -0.03 0.07 0.79 0.206 1.4
choices -0.12 0.02 0.79 0.213 1.6
self_restraint 0.06 -0.01 0.85 0.153 2.1
intentions -0.07 -0.04 0.77 0.230 1.4
goal 0.04 -0.07 0.70 0.297 2.3
conscious 0.04 -0.05 0.73 0.266 1.2
self_aware 0.08 -0.04 0.82 0.185 1.8
desires -0.01 -0.07 0.84 0.163 1.2
embarrassed -0.04 0.03 0.89 0.109 2.5
emo_recog 0.07 0.00 0.75 0.250 1.6
joy -0.05 -0.03 0.90 0.103 1.3
morality -0.09 0.01 0.88 0.117 2.5
personality 0.06 0.03 0.86 0.135 1.2
pleasure -0.01 -0.02 0.89 0.113 1.2
pride 0.02 0.00 0.81 0.189 1.5
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11 MR12
SS loadings 24.49 3.38 2.06 0.74 0.48 0.31 0.27 0.24 0.21 0.17 0.16 0.15
Proportion Var 0.61 0.08 0.05 0.02 0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.00
Cumulative Var 0.61 0.70 0.75 0.77 0.78 0.79 0.79 0.80 0.80 0.81 0.81 0.82
Proportion Explained 0.75 0.10 0.06 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00
Cumulative Proportion 0.75 0.85 0.91 0.93 0.95 0.96 0.97 0.97 0.98 0.99 0.99 1.00
MR13
SS loadings 0.14
Proportion Var 0.00
Cumulative Var 0.82
Proportion Explained 0.00
Cumulative Proportion 1.00
Mean item complexity = 1.7
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 52.07 with Chi Square of 21651.8
The degrees of freedom for the model are 338 and the objective function was 1.29
The root mean square of the residuals (RMSR) is 0.01
The df corrected root mean square of the residuals is 0.01
The harmonic number of observations is 431 with the empirical chi square 34.49 with prob < 1
The total number of observations was 431 with Likelihood Chi Square = 526.8 with prob < 2e-10
Tucker Lewis Index of factoring reliability = 0.979
RMSEA index = 0.039 and the 90 % confidence intervals are 0.03 0.042
BIC = -1523.55
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5 MR6 MR7
Correlation of scores with factors 1.00 0.98 0.96 0.89 0.88 0.80 0.77
Multiple R square of scores with factors 0.99 0.96 0.92 0.80 0.77 0.64 0.60
Minimum correlation of possible factor scores 0.99 0.91 0.83 0.60 0.55 0.28 0.19
MR8 MR9 MR10 MR11 MR12 MR13
Correlation of scores with factors 0.79 0.76 0.71 0.72 0.66 0.67
Multiple R square of scores with factors 0.62 0.59 0.51 0.51 0.44 0.45
Minimum correlation of possible factor scores 0.24 0.17 0.01 0.03 -0.12 -0.10
[1] 3[1] 3Step 2: Run EFA with varimax rotation
Factor Analysis using method = minres
Call: fa(r = d4_all, nfactors = nfactors_d4_all, rotate = chosenRotType,
fm = "minres", cor = chosenCorType)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 h2 u2 com
happy 0.74 0.48 0.23 0.84 0.16 1.9
depressed 0.52 0.62 0.23 0.70 0.30 2.2
fear 0.83 0.20 0.37 0.86 0.14 1.5
angry 0.68 0.46 0.31 0.76 0.24 2.2
calm 0.75 0.35 0.32 0.79 0.21 1.8
sounds 0.45 0.08 0.75 0.76 0.24 1.7
seeing 0.56 0.13 0.67 0.78 0.22 2.0
temperature 0.40 0.06 0.71 0.68 0.32 1.6
odors 0.58 0.22 0.58 0.72 0.28 2.3
depth 0.29 0.32 0.68 0.65 0.35 1.8
computations -0.40 0.51 0.39 0.58 0.42 2.8
thoughts 0.60 0.49 0.33 0.72 0.28 2.5
reasoning 0.17 0.66 0.48 0.70 0.30 2.0
remembering 0.26 0.41 0.71 0.74 0.26 1.9
beliefs 0.19 0.83 0.14 0.74 0.26 1.2
hungry 0.84 0.11 0.33 0.83 0.17 1.3
tired 0.85 0.21 0.36 0.89 0.11 1.5
pain 0.86 0.14 0.33 0.87 0.13 1.3
nauseated 0.70 0.40 0.27 0.71 0.29 1.9
safe 0.74 0.32 0.36 0.78 0.22 1.9
love 0.65 0.56 0.18 0.77 0.23 2.1
recognizing 0.43 0.38 0.59 0.67 0.33 2.6
communicating 0.35 0.30 0.71 0.71 0.29 1.8
guilt 0.31 0.82 0.13 0.79 0.21 1.3
disrespected 0.30 0.78 0.18 0.73 0.27 1.4
free_will 0.60 0.42 0.38 0.68 0.32 2.6
choices 0.38 0.37 0.67 0.73 0.27 2.2
self_restraint 0.24 0.71 0.38 0.70 0.30 1.8
intentions 0.49 0.47 0.44 0.66 0.34 3.0
goal 0.23 0.41 0.57 0.55 0.45 2.2
conscious 0.65 0.35 0.38 0.69 0.31 2.2
self_aware 0.38 0.60 0.35 0.63 0.37 2.4
desires 0.69 0.43 0.34 0.78 0.22 2.2
embarrassed 0.29 0.82 0.09 0.77 0.23 1.3
emo_recog 0.33 0.72 0.30 0.71 0.29 1.8
joy 0.72 0.51 0.25 0.84 0.16 2.1
morality 0.17 0.81 0.25 0.74 0.26 1.3
personality 0.64 0.52 0.33 0.80 0.20 2.5
pleasure 0.79 0.35 0.35 0.87 0.13 1.8
pride 0.43 0.74 0.20 0.78 0.22 1.8
MR1 MR2 MR3
SS loadings 12.24 10.06 7.40
Proportion Var 0.31 0.25 0.19
Cumulative Var 0.31 0.56 0.74
Proportion Explained 0.41 0.34 0.25
Cumulative Proportion 0.41 0.75 1.00
Mean item complexity = 1.9
Test of the hypothesis that 3 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 52.07 with Chi Square of 21651.8
The degrees of freedom for the model are 663 and the objective function was 5.44
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is 0.03
The harmonic number of observations is 431 with the empirical chi square 406.4 with prob < 1
The total number of observations was 431 with Likelihood Chi Square = 2251.71 with prob < 1e-171
Tucker Lewis Index of factoring reliability = 0.91
RMSEA index = 0.077 and the 90 % confidence intervals are 0.071 0.078
BIC = -1770.12
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3
Correlation of scores with factors 0.98 0.97 0.95
Multiple R square of scores with factors 0.95 0.95 0.91
Minimum correlation of possible factor scores 0.91 0.90 0.81Factor loadings table
Big factor loadings table for all studies (Studies 1-4)
Joining, by = "mc"
Joining, by = "mc"
Joining, by = "mc"Figures
Joining, by = "condition"
Column `condition` joining factor and character vector, coercing into character vectorJoining, by = "condition"
Column `condition` joining factor and character vector, coercing into character vectorJoining, by = "condition"
Column `condition` joining factor and character vector, coercing into character vector
|================== | 31% ~4 s remaining
|================== | 32% ~4 s remaining
|================== | 32% ~4 s remaining
|=================== | 33% ~4 s remaining
|=================== | 34% ~4 s remaining
|==================== | 35% ~4 s remaining
|==================== | 36% ~4 s remaining
|===================== | 37% ~4 s remaining
|===================== | 38% ~4 s remaining
|====================== | 39% ~4 s remaining
|====================== | 40% ~4 s remaining
|======================= | 40% ~4 s remaining
|======================= | 41% ~4 s remaining
|======================== | 42% ~4 s remaining
|======================== | 43% ~4 s remaining
|========================= | 44% ~4 s remaining
|========================= | 44% ~4 s remaining
|========================= | 45% ~4 s remaining
|========================== | 45% ~4 s remaining
|========================== | 46% ~4 s remaining
|=========================== | 47% ~4 s remaining
|=========================== | 48% ~3 s remaining
|============================ | 49% ~3 s remaining
|============================ | 50% ~3 s remaining
|============================= | 51% ~3 s remaining
|============================= | 51% ~3 s remaining
|============================== | 52% ~3 s remaining
|============================== | 53% ~3 s remaining
|=============================== | 54% ~3 s remaining
|=============================== | 55% ~3 s remaining
|================================ | 56% ~3 s remaining
|================================ | 57% ~3 s remaining
|================================= | 58% ~3 s remaining
|================================= | 58% ~3 s remaining
|================================== | 59% ~3 s remaining
|================================== | 60% ~3 s remaining
|=================================== | 60% ~3 s remaining
|=================================== | 61% ~3 s remaining
|=================================== | 62% ~3 s remaining
|==================================== | 62% ~2 s remaining
|==================================== | 63% ~2 s remaining
|===================================== | 64% ~2 s remaining
|===================================== | 65% ~2 s remaining
|====================================== | 66% ~2 s remaining
|====================================== | 67% ~2 s remaining
|======================================= | 67% ~2 s remaining
|======================================= | 68% ~2 s remaining
|======================================== | 69% ~2 s remaining
|======================================== | 70% ~2 s remaining
|======================================== | 70% ~2 s remaining
|========================================= | 71% ~2 s remaining
|========================================= | 72% ~2 s remaining
|========================================== | 73% ~2 s remaining
|========================================== | 74% ~2 s remaining
|=========================================== | 75% ~2 s remaining
|=========================================== | 75% ~2 s remaining
|============================================ | 76% ~2 s remaining
|============================================ | 77% ~2 s remaining
|============================================ | 77% ~2 s remaining
|============================================= | 78% ~1 s remaining
|============================================= | 79% ~1 s remaining
|============================================== | 79% ~1 s remaining
|============================================== | 80% ~1 s remaining
|============================================== | 80% ~1 s remaining
|============================================== | 81% ~1 s remaining
|=============================================== | 81% ~1 s remaining
|=============================================== | 82% ~1 s remaining
|=============================================== | 82% ~1 s remaining
|================================================ | 83% ~1 s remaining
|================================================ | 84% ~1 s remaining
|================================================ | 84% ~1 s remaining
|================================================= | 85% ~1 s remaining
|================================================= | 86% ~1 s remaining
|================================================== | 86% ~1 s remaining
|================================================== | 87% ~1 s remaining
|================================================== | 88% ~1 s remaining
|=================================================== | 88% ~1 s remaining
|=================================================== | 89% ~1 s remaining
|=================================================== | 89% ~1 s remaining
|==================================================== | 90% ~1 s remaining
|==================================================== | 91% ~1 s remaining
|===================================================== | 91% ~1 s remaining
|===================================================== | 92% ~1 s remaining
|===================================================== | 93% ~1 s remaining
|====================================================== | 93% ~0 s remaining
|====================================================== | 94% ~0 s remaining
|====================================================== | 95% ~0 s remaining
|======================================================= | 95% ~0 s remaining
|======================================================= | 96% ~0 s remaining
|======================================================= | 96% ~0 s remaining
|======================================================== | 97% ~0 s remaining
|======================================================== | 98% ~0 s remaining
|======================================================== | 98% ~0 s remaining
|========================================================= | 99% ~0 s remaining
|========================================================= | 99% ~0 s remaining Joining, by = "condition"
Column `condition` joining factor and character vector, coercing into character vectorJoining, by = c("condition", "mc", "Mean", "Lower", "Upper", "n", "study")
Joining, by = c("condition", "mc", "Mean", "Lower", "Upper", "n", "study")
Joining, by = c("condition", "mc", "Mean", "Lower", "Upper", "n", "study")Joining, by = "mc"
Joining, by = "mc"
Joining, by = "mc"
Joining, by = "mc"
Joining, by = "mc"Figure 1
Mean ratings of 40 mental capacities for a subset of the 21 entities included in Study 4. (See Fig. S5 for mean ratings for the full set of entities.) Participants responded on a scale from 0 (Not at all capable) to 6 (Highly capable). Error bars are nonparametric bootstrapped 95% confidence intervals. Mental capacities are grouped according to their dominant factor loading in Study 1. Doing computations was the only item to load negatively on its dominant factor in any study (and did so in Studies 1-3); in Study 4, it loaded positively on its dominant factor (Factor 2, “heart”).
Figure S1 (supporting materials)
Factor loadings for the 40 mental capacities on the three rotated factors in Study 1. Items are colored by their dominant factor loading: Items that loaded most strongly on the body factor (physiological states and will) are in red; items that loaded most strongly on the heart factor (social-emotional experiences and morality) are in blue; and items that loaded most strongly on the mind factor (perceptual-cognitive abilities and goal pursuit) are in green.
Joining, by = "item"Figure S2 (supporting materials)
Mean ratings of 40 mental capacities for the 2 entities included in Studies 1-3. Participants responded on a scale from 0 (Not at all capable) to 6 (Highly capable). Error bars are nonparametric bootstrapped 95% confidence intervals. Mental capacities are grouped according to their dominant factor loading in Study 1.
Figure S3 (supporting materials)
Mean ratings of 40 mental capacities for all 21 entities included in Study 4. Participants responded on a scale from 0 (Not at all capable) to 6 (Highly capable). Error bars are nonparametric bootstrapped 95% confidence intervals. Mental capacities are grouped according to their dominant factor loading in Study 4.
---
title: "Body, Heart, & Mind (Weisman, Dweck, & Markman, submitted)"
output:
  html_notebook:
    theme: flatly
    toc: yes
  html_document:
    toc: yes
  pdf_document:
    toc: yes
---

```{r global_options, include = FALSE}
knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE,
                      fig.width = 10, fig.height = 30)
```

# Setup

```{r workspace setup}
# load libraries
library(tidyverse)
library(psych)
library(langcog) # source: https://github.com/langcog/langcog
library(rms)
library(scatterplot3d)
library(lattice)
library(directlabels)
library(plotly)
library(RColorBrewer)

# clear workspace
# rm(list = ls(all = T))
# graphics.off()
```

```{r functions}
# make rounding function
round2 <- function(x) {format(round(x, 2), nsmall = 2)}

# make cleanup function
cleanup <- function(datasource) {
  if(datasource %in% c("study 1", "study 2")) {
    
    # set target dataset
    if(datasource == "study 1"){d <- d_raw_study1}
    if(datasource == "study 2"){d <- d_raw_study2}
    
    # enact exclusionary criteria
    d_clean_1 <- d %>%
      mutate(finished_mod = ifelse(is.na(CATCH), 0,
                                   ifelse(finished == 1, 1,
                                          0.5))) %>%
      filter(CATCH == 1, # exclude Ps who fail catch trials 
             finished_mod != 0) %>% # exclude Ps who did not complete task
      mutate(yob_correct = as.numeric(
        ifelse(as.numeric(as.character(yob)) > 1900 & 
                 as.numeric(as.character(yob)) < 2000, 
               as.numeric(as.character(yob)), NA)), # correct formatting in yob
        age_approx = 2016 - yob_correct) %>% # calculate approximate age
      mutate(gender = factor(gender, levels = c(1, 2, 0), 
                             labels = c("m", "f", "other"))) %>%
      filter(age_approx >= 18) # exclude Ps who are younger than 18 years
    
    # recode background and demographic variables
    d_clean <- d_clean_1 %>%
      mutate( # deal with study number
        study = factor(study)) %>%
      mutate( # deal with study duration
        duration = as.numeric(difftime(strptime(end_time, "%I:%M:%S"),
                                       strptime(start_time, "%I:%M:%S"),
                                       units = "mins"))) %>%
      mutate( # deal with race
        race_asian_east = 
          factor(ifelse(is.na(race_asian_east), "", "asian_east ")),
        race_asian_south = 
          factor(ifelse(is.na(race_asian_south), "", "asian_south ")),
        race_asian_other = 
          factor(ifelse(is.na(race_asian_other), "", "asian_other ")),
        race_black = 
          factor(ifelse(is.na(race_black), "", "black ")),
        race_hispanic = 
          factor(ifelse(is.na(race_hispanic), "", "hispanic ")),
        race_middle_eastern = 
          factor(ifelse(is.na(race_middle_eastern), "", "middle_eastern ")),
        race_native_american = 
          factor(ifelse(is.na(race_native_american), "", "native_american ")),
        race_pac_islander = 
          factor(ifelse(is.na(race_pac_islander), "", "pac_islander ")),
        race_white = 
          factor(ifelse(is.na(race_white), "", "white ")),
        race_other_prefno = 
          factor(ifelse(is.na(race_other_prefno), "", "other_prefno ")),
        race_cat = paste0(race_asian_east, race_asian_south, race_asian_other,
                          race_black, race_hispanic, race_middle_eastern,
                          race_native_american, race_pac_islander, race_white,
                          race_other_prefno),
        race_cat2 = factor(sub(" +$", "", race_cat)),
        race_cat3 = factor(ifelse(grepl(" ", race_cat2) == T, "multiracial",
                                  as.character(race_cat2)))) %>%
      dplyr::select(study, subid:end_time, duration, finished:gender, 
             religion_buddhism:age_approx, race_cat3) %>%
      rename(race_cat = race_cat3) %>%
      mutate( # deal with religion
        religion_buddhism = 
          factor(ifelse(is.na(religion_buddhism), "", "buddhism ")),
        religion_christianity = 
          factor(ifelse(is.na(religion_christianity), "", "christianity ")),
        religion_hinduism = 
          factor(ifelse(is.na(religion_hinduism), "", "hinduism ")),
        religion_islam = 
          factor(ifelse(is.na(religion_islam), "", "islam ")),
        religion_jainism = 
          factor(ifelse(is.na(religion_jainism), "", "jainism ")),
        religion_judaism = 
          factor(ifelse(is.na(religion_judaism), "", "judaism ")),
        religion_sikhism = 
          factor(ifelse(is.na(religion_sikhism), "", "sikhism ")),
        religion_other = 
          factor(ifelse(is.na(religion_other), "", "other ")),
        religion_none = 
          factor(ifelse(is.na(religion_none), "", "none ")),
        religion_prefno = 
          factor(ifelse(is.na(religion_prefno), "", "other_prefno ")),
        religion_cat = paste0(religion_buddhism, religion_christianity, 
                              religion_hinduism, religion_islam, 
                              religion_jainism, religion_judaism, 
                              religion_sikhism, religion_other, 
                              religion_none, religion_prefno),
        religion_cat2 = factor(sub(" +$", "", religion_cat)),
        religion_cat3 = factor(ifelse(grepl(" ", religion_cat2) == T, 
                                      "multireligious",
                                      as.character(religion_cat2)))) %>%
      dplyr::select(study:gender, feedback:race_cat, religion_cat3) %>%
      rename(religion_cat = religion_cat3)
    
    # remove extraneous dfs and variables
    rm(d, d_clean_1)
  }
  
  if(datasource == "study 3") {
    
    # set target dataset
    d <- d_raw_study3
    
    # enact exclusionary criteria
    d_clean_1 <- d %>%
      mutate(finished_mod = ifelse(is.na(CATCH..characterL) | 
                                     is.na(CATCH..characterR), 0,
                                   ifelse(finished == 1, 1,
                                          0.5))) %>%
      filter(CATCH..characterL == 5, # exclude Ps who fail catch trials 
             CATCH..characterR == 5,
             finished_mod != 0) %>% # exclude Ps who did not complete task
      mutate(yob_correct = as.numeric(
        ifelse(as.numeric(as.character(yob)) > 1900 & 
                 as.numeric(as.character(yob)) < 2000, 
               as.numeric(as.character(yob)), NA)), # correct formatting in yob
        age_approx = 2016 - yob_correct) %>% # calculate approximate age
      mutate(gender = factor(gender, levels = c(1, 2, 0), 
                             labels = c("m", "f", "other"))) %>%
      filter(age_approx >= 18) # exclude Ps who are younger than 18 years
    
    # recode background and demographic variables
    d_clean_2 <- d_clean_1 %>%
      mutate( # deal with study number
        study = factor(study)) %>%
      mutate( # deal with study duration
        duration = as.numeric(difftime(strptime(end_time, "%I:%M:%S"),
                                       strptime(start_time, "%I:%M:%S"),
                                       units = "mins"))) %>%
      mutate( # deal with race
        race_asian_east = 
          factor(ifelse(is.na(race_asian_east), "", "asian_east ")),
        race_asian_south = 
          factor(ifelse(is.na(race_asian_south), "", "asian_south ")),
        race_asian_other = 
          factor(ifelse(is.na(race_asian_other), "", "asian_other ")),
        race_black = 
          factor(ifelse(is.na(race_black), "", "black ")),
        race_hispanic = 
          factor(ifelse(is.na(race_hispanic), "", "hispanic ")),
        race_middle_eastern = 
          factor(ifelse(is.na(race_middle_eastern), "", "middle_eastern ")),
        race_native_american = 
          factor(ifelse(is.na(race_native_american), "", "native_american ")),
        race_pac_islander = 
          factor(ifelse(is.na(race_pac_islander), "", "pac_islander ")),
        race_white = 
          factor(ifelse(is.na(race_white), "", "white ")),
        race_other_prefno = 
          factor(ifelse(is.na(race_other_prefno), "", "other_prefno ")),
        race_cat = paste0(race_asian_east, race_asian_south, race_asian_other,
                          race_black, race_hispanic, race_middle_eastern,
                          race_native_american, race_pac_islander, race_white,
                          race_other_prefno),
        race_cat2 = factor(sub(" +$", "", race_cat)),
        race_cat3 = factor(ifelse(grepl(" ", race_cat2) == T, "multiracial",
                                  as.character(race_cat2)))) %>%
      dplyr::select(study, subid:end_time, duration, finished:gender, 
             religion_buddhism:age_approx, race_cat3) %>%
      rename(race_cat = race_cat3) %>%
      mutate( # deal with religion
        religion_buddhism = 
          factor(ifelse(is.na(religion_buddhism), "", "buddhism ")),
        religion_christianity = 
          factor(ifelse(is.na(religion_christianity), "", "christianity ")),
        religion_hinduism = 
          factor(ifelse(is.na(religion_hinduism), "", "hinduism ")),
        religion_islam = 
          factor(ifelse(is.na(religion_islam), "", "islam ")),
        religion_jainism = 
          factor(ifelse(is.na(religion_jainism), "", "jainism ")),
        religion_judaism = 
          factor(ifelse(is.na(religion_judaism), "", "judaism ")),
        religion_sikhism = 
          factor(ifelse(is.na(religion_sikhism), "", "sikhism ")),
        religion_other = 
          factor(ifelse(is.na(religion_other), "", "other ")),
        religion_none = 
          factor(ifelse(is.na(religion_none), "", "none ")),
        religion_prefno = 
          factor(ifelse(is.na(religion_prefno), "", "other_prefno ")),
        religion_cat = paste0(religion_buddhism, religion_christianity, 
                              religion_hinduism, religion_islam, 
                              religion_jainism, religion_judaism, 
                              religion_sikhism, religion_other, 
                              religion_none, religion_prefno),
        religion_cat2 = factor(sub(" +$", "", religion_cat)),
        religion_cat3 = factor(ifelse(grepl(" ", religion_cat2) == T, 
                                      "multireligious",
                                      as.character(religion_cat2)))) %>%
      dplyr::select(study:gender, feedback:race_cat, religion_cat3) %>%
      rename(religion_cat = religion_cat3)
    
    # rename response variables
    d_clean_3 <- d_clean_2
    names(d_clean_3) <- gsub("get", "", names(d_clean_3))
    names(d_clean_3) <- gsub("\\.", "", names(d_clean_3))
    names(d_clean_3) <- gsub("char", "_char", names(d_clean_3))
    names(d_clean_3)[names(d_clean_3) %in% c("_characterL", "_characterR")] <- 
      c("characterL", "characterR")
    
    # recode response variables (center)
    d_clean_4 <- d_clean_3
    for(i in 11:92) {
      d_clean_4[,i] <- d_clean_4[,i] - 4 # transform from 1 to 7 --> -3 to 3
    }
    
    # recode characterL vs. characterR as beetle vs. robot
    d_clean_5_demo <- d_clean_4 %>%
      dplyr::select(study:condition, yob:religion_cat)
    
    d_clean_5_characterL <- d_clean_4 %>%
      mutate(target = characterL) %>%
      dplyr::select(study, subid, target, happy_characterL:CATCH_characterL)
    names(d_clean_5_characterL) <- gsub("_characterL", "", 
                                        names(d_clean_5_characterL))
    
    d_clean_5_characterR <- d_clean_4 %>%
      mutate(target = characterR) %>%
      dplyr::select(study, subid, target, happy_characterR:CATCH_characterR)
    names(d_clean_5_characterR) <- gsub("_characterR", "", 
                                        names(d_clean_5_characterR))
    
    d_clean <- d_clean_5_characterL %>%
      full_join(d_clean_5_characterR) %>%
      full_join(d_clean_5_demo) %>%
      dplyr::select(study, subid, date:religion_cat, target:CATCH)
    
    # remove extraneous dfs and variables
    rm(d, d_clean_1, d_clean_2, d_clean_3, d_clean_4, d_clean_5_characterL, 
       d_clean_5_characterR, d_clean_5_demo, i)
  }
  
  if(datasource == "study 4") {
    
    # set target dataset
    d <- d_raw_study4

        # enact exclusionary criteria
    d_clean_1 <- d %>%
      mutate(finished_mod = ifelse(is.na(CATCH), 0,
                                   ifelse(finished == 1, 1,
                                          0.5))) %>%
      filter(CATCH == 1, # exclude Ps who fail catch trials 
             finished_mod != 0) %>% # exclude Ps who did not complete task
      mutate(yob_correct = as.numeric(
        ifelse(as.numeric(as.character(yob)) > 1900 & 
                 as.numeric(as.character(yob)) < 2000, 
               as.numeric(as.character(yob)), NA)), # correct formatting in yob
        age_approx = 2016 - yob_correct) %>% # calculate approximate age
      mutate(gender = factor(gender, levels = c(1, 2, 0), 
                             labels = c("m", "f", "other"))) %>%
      filter(age_approx >= 18) # exclude Ps who are younger than 18 years
    
    # recode one character
    d_clean_2 <- d_clean_1 %>%
      mutate(condition = factor(ifelse(
        grepl("vegetative", as.character(condition)), "pvs",
        ifelse(grepl("blue", as.character(condition)), "bluejay",
               ifelse(grepl("chimp", as.character(condition)), "chimp",
                      as.character(condition))))))

    # recode background and demographic variables
    d_clean <- d_clean_2 %>%
      mutate( # deal with study number
        study = factor(study)) %>%
      mutate( # deal with study duration
        duration = as.numeric(difftime(strptime(end_time, "%I:%M:%S"),
                                       strptime(start_time, "%I:%M:%S"),
                                       units = "mins"))) %>%
      mutate( # deal with race
        race_asian_east = 
          factor(ifelse(is.na(race_asian_east), "", "asian_east ")),
        race_asian_south = 
          factor(ifelse(is.na(race_asian_south), "", "asian_south ")),
        race_asian_other = 
          factor(ifelse(is.na(race_asian_other), "", "asian_other ")),
        race_black = 
          factor(ifelse(is.na(race_black), "", "black ")),
        race_hispanic = 
          factor(ifelse(is.na(race_hispanic), "", "hispanic ")),
        race_middle_eastern = 
          factor(ifelse(is.na(race_middle_eastern), "", "middle_eastern ")),
        race_native_american = 
          factor(ifelse(is.na(race_native_american), "", "native_american ")),
        race_pac_islander = 
          factor(ifelse(is.na(race_pac_islander), "", "pac_islander ")),
        race_white = 
          factor(ifelse(is.na(race_white), "", "white ")),
        race_other_prefno = 
          factor(ifelse(is.na(race_other_prefno), "", "other_prefno ")),
        race_cat = paste0(race_asian_east, race_asian_south, race_asian_other,
                          race_black, race_hispanic, race_middle_eastern,
                          race_native_american, race_pac_islander, race_white,
                          race_other_prefno),
        race_cat2 = factor(sub(" +$", "", race_cat)),
        race_cat3 = factor(ifelse(grepl(" ", race_cat2) == T, "multiracial",
                                  as.character(race_cat2)))) %>%
      dplyr::select(study, subid:end_time, duration, finished:gender, 
             education:age_approx, race_cat3) %>%
      rename(race_cat = race_cat3)
    
    # filter conditions if desired
    if(is.element("none", chosenExclude)) {} else {
      d_clean <- d_clean %>%
        filter(!condition %in% chosenExclude)
    }
    
    # remove extraneous dfs and variables
    rm(d, d_clean_1, d_clean_2)
  }
  
#   # transform to 0 to 6 scale
#   d_clean <- d_clean %>%
#     gather(mc, score, happy:pride) %>%
#     mutate(score = score + 3) %>% # transform from -3 to 3 --> 0 to 6
#     spread(mc, score)
  
  # remove outliers
  if(chosenOutlierHandling == "remove") {
    
    if(datasource %in% c("study 1", "study 2", "study 4")) {
        d_clean <- d_clean %>%
        gather(mc, score, happy:pride) %>%
        group_by(condition, mc) %>%
        filter(!score %in% boxplot.stats(score, 2.5)$out) %>%
        spread(mc, score) %>%
        arrange(condition, subid)
    }
    
    if(datasource == "study 3") {
      d_clean <- d_clean %>%
        gather(mc, score, happy:pride) %>%
        group_by(target, mc) %>%
        filter(!score %in% boxplot.stats(score, 2.5)$out) %>%
        spread(mc, score) %>%
        arrange(target, subid)
    }
    
  }
  
  # filter items if desired
  if(is.element("none", chosenExcludeItem)) {} else {
    d_clean <- d_clean %>%
      dplyr::select(-contains(chosenExcludeItem))
  }

  # return cleaned dataset
  return(d_clean)
}

# make function for examining exclusion of participants
excludedCounts <- function(datasource) {
  
  # set datasource
  if(datasource == "study 1"){
    d <- d1
    d_raw <- d_raw_study1
  }
  if(datasource == "study 2"){
    d <- d2
    d_raw <- d_raw_study2
  }
  if(datasource == "study 3"){
    d <- d3
    d_raw <- d_raw_study3
  }
  if(datasource == "study 4"){
    d <- d4
    d_raw <- d_raw_study4
  }
  
  # get subids of successful participants
  d_subids <- levels(factor(as.character(d$subid)))
  
  # get subids of excluded participants
  d_excluded <- d_raw %>%
    filter(is.element(subid, d_subids) == FALSE) %>%
    dplyr::select(condition, subid, finished, starts_with("CATCH"), yob)

  # count excluded participants
  d_excluded_n <- length(d_excluded$subid)
  
  if(datasource %in% c("study 1", "study 2", "study 4")) {
    # count participants who did not finish
    d_excluded_unfinished <- d_excluded %>%
      filter(is.na(CATCH) == T,
             finished != 1) %>%
      dplyr::select(subid) %>%
      c()
    
    # count participants who finished, but failed catch trial
    d_excluded_CATCH <- d_excluded %>%
      filter(is.element(subid, d_excluded_unfinished$subid) == FALSE) %>%
      filter(CATCH != 1) %>%
      dplyr::select(subid) %>%
      c()
  }
  
  if(datasource == "study 3") {
    # count participants who did not finish
    d_excluded_unfinished <- d_excluded %>%
      filter(is.na(CATCH..characterL) == T,
             is.na(CATCH..characterR) == T,
             finished != 1) %>%
      dplyr::select(subid) %>%
      c()
    
    # count participants who finished, but failed catch trial
    d_excluded_CATCH <- d_excluded %>%
      filter(is.element(subid, d_excluded_unfinished$subid) == FALSE) %>%
      filter(CATCH..characterL != 5 | CATCH..characterR != 5) %>%
      dplyr::select(subid) %>%
      c()
  }
  
  # count participants who finished and passed catch trial, 
  # but did not provide year of birth
  d_excluded_no_yob <- d_excluded %>%
    filter(is.element(subid, d_excluded_unfinished$subid) == FALSE,
           is.element(subid, d_excluded_CATCH$subid) == FALSE) %>%
    mutate(yob = as.numeric(as.character(yob))) %>%
    filter(is.na(yob) | yob < 1899 | nchar(as.character(yob)) != 4) %>%
    dplyr::select(subid) %>%
    c()
  
  # count participants who finished and passed catch trial, 
  # but did not provide year of birth
  d_excluded_young <- d_excluded %>%
    filter(is.element(subid, d_excluded_unfinished$subid) == FALSE,
           is.element(subid, d_excluded_CATCH$subid) == FALSE,
           is.element(subid, d_excluded_no_yob$subid) == FALSE) %>%
    mutate(yob = as.numeric(as.character(yob))) %>%
    filter(is.na(yob) | 2016 - yob < 18) %>%
    dplyr::select(subid) %>%
    c()
  
  # sum up excluded participants by category
  total <- sum(length(d_excluded_unfinished$subid),
               length(d_excluded_CATCH$subid),
               length(d_excluded_no_yob$subid),
               length(d_excluded_young$subid))
  
  # calculate counts
  excluded_counts <- 
    data.frame("did_not_finish" = length(d_excluded_unfinished$subid),
               "failed_catch_trial" = length(d_excluded_CATCH$subid),
               "did_not_provide_yob" = length(d_excluded_no_yob$subid),
               "too_young" = length(d_excluded_young$subid),
               "TOTAL_excluded" = total,
               "TOTAL_participated" = length(d$subid),
               "OVERALL_TOTAL" = sum(total, length(d$subid)))
  
  if(total != d_excluded_n) {
    stop("Error: 4 sources of exclusion do not add up to total.")
    } else {
      return(excluded_counts)
    }
}

# make function for stripping dataframes for dimension reducation
makeDRDF <- function(datasource, chosenCondition) {
  
  # set target dataset
  if(datasource == "study 1"){d <- d1}
  if(datasource == "study 2"){d <- d2}
  if(datasource == "study 3"){
    # rename variables for ease of function applpication
    d <- d3 %>%
      rename(order = condition,
             condition = target)
    
    # rename subids by condition if collapses across conditions
    d <- d %>%
      mutate(subid = paste(condition, subid, sep = "_"))
  }
  if(datasource == "study 4"){d <- d4}
  
  # filter by condition if specified
  if(chosenCondition %in% c("beetle", "robot")) {
    d <- d %>% filter(condition == chosenCondition)
  }
  
  # make stripped dataframe for dimension reducation analyses
  d_strip <- d %>%
    dplyr::select(subid, happy:pride)
  d_strip <- data.frame(d_strip[,-1], row.names = d_strip$subid)
  
  # return stripped dataframe
  return(d_strip)
}

# make demographics functions
demoSampleSize <- function(datasource) {

  # set target dataset
  if(datasource == "study 1"){d <- d1}
  if(datasource == "study 2"){d <- d2}
  if(datasource == "study 3"){
    # rename variables for ease of function applpication
    d <- d3 %>%
      rename(order = condition,
             condition = target)
  }
  if(datasource == "study 4"){d <- d4}

  # get sample size per condition
  sample_size <- vector()
  for(i in levels(d$condition)) {
    sample_size[as.character(i)] <- 
      as.numeric(d %>% filter(condition == i) %>% dplyr::select(subid) %>% 
                   unique() %>% count())
  }

  # add total sample size  
  sample_size["all"] <- as.numeric(d %>% dplyr::select(subid) %>% 
                                     unique() %>% count())
  
  # make into dataframe for using kable
  sample_size <- data.frame(sample_size) %>%
    rownames_to_column() %>%
    rename(condition = rowname,
           n = sample_size)
  
  # return dataframe for using kable
  return(sample_size)
}
demoDuration <- function(datasource) {

  # set target dataset
  if(datasource == "study 1"){d <- d1}
  if(datasource == "study 2"){d <- d2}
  if(datasource == "study 3"){
    # recode variables for ease of function applpication
    d <- d3 %>%
      mutate(condition = "within-subjects")
  }
  if(datasource == "study 4"){d <- d4}

  # get sample size per condition
  duration <- d %>%
    group_by(condition) %>%
    summarise(min_duration = min(duration),
              max_duration = max(duration),
              median_duration = median(duration),
              mean_duration = mean(duration),
              sd_duration = sd(duration))

  # add total duration
  if(datasource %in% c("study 1", "study 2", "study 4")) {
    all <- d %>%
      summarise(min_duration = min(duration),
                max_duration = max(duration),
                median_duration = median(duration),
                mean_duration = mean(duration),
                sd_duration = sd(duration)) %>%
      mutate(condition = "all")
    duration <- rbind(duration, all) # not sure why full_join doesn't work    
  }

  # return dataframe for using kable
  return(duration)
}
demoAge <- function(datasource) {

  # set target dataset
  if(datasource == "study 1"){d <- d1}
  if(datasource == "study 2"){d <- d2}
  if(datasource == "study 3"){
    # recode variables for ease of function applpication
    d <- d3 %>%
      mutate(condition = "within-subjects")
  }
  if(datasource == "study 4"){d <- d4}

  # get sample size per condition
  age <- d %>%
    group_by(condition) %>%
    summarise(min_age = min(age_approx),
              max_age = max(age_approx),
              median_age = median(age_approx),
              mean_age = mean(age_approx),
              sd_age = sd(age_approx))

  # add total age
  if(datasource %in% c("study 1", "study 2", "study 4")) {
    all <- d %>%
      summarise(min_age = min(age_approx),
                max_age = max(age_approx),
                median_age = median(age_approx),
                mean_age = mean(age_approx),
                sd_age = sd(age_approx)) %>%
      mutate(condition = "all")
    age <- full_join(age, all)
  }

  # return dataframe for using kable
  return(age)
}
demoGender <- function(datasource) {

  # set target dataset
  if(datasource == "study 1"){d <- d1}
  if(datasource == "study 2"){d <- d2}
  if(datasource == "study 3"){d <- d3}
  if(datasource == "study 4"){d <- d4}

  # get gender per condition and overall
  if(datasource %in% c("study 1", "study 2", "study 4")) {
    gender <- data.frame(addmargins(with(d, table(condition, gender)))) %>%
      filter(gender != "Sum") %>%
      rename(n = Freq)
  }
  
  if(datasource == "study 3") {
    gender <- data.frame(with(d, table(gender))) %>%
      rename(n = Freq) %>%
      mutate(condition = "Sum") %>%
      dplyr::select(condition, gender, n)
  }
  
  if(datasource %in% c("study 1", "study 2", "study 3")) {
    gender <- gender %>%
    mutate(condition = factor(ifelse(condition == "Sum", 
                                     "all", as.character(condition)),
                              levels = c("beetle", "robot", "all"))) %>%
    arrange(condition, gender) %>%
    spread(gender, n)
  }
  
  if(datasource == "study 4") {
    gender <- gender %>%
    mutate(condition = factor(ifelse(condition == "Sum", 
                                     "all", as.character(condition)),
                              levels = c(levels(d$condition), "all"))) %>%
    arrange(condition, gender) %>%
    spread(gender, n)
  }
  
  # return dataframe for using kable
  return(gender)
}
demoRace <- function(datasource) {

  # set target dataset
  if(datasource == "study 1"){d <- d1}
  if(datasource == "study 2"){d <- d2}
  if(datasource == "study 3"){d <- d3}
  if(datasource == "study 4"){d <- d4}

  # get race per condition and overall
  if(datasource %in% c("study 1", "study 2", "study 4")) {
    race <- data.frame(addmargins(with(d, table(condition, race_cat)))) %>%
      filter(race_cat != "Sum") %>%
      rename(n = Freq)
  }
  
  if(datasource == "study 3") {
    race <- data.frame(with(d, table(race_cat))) %>%
      rename(n = Freq) %>%
      mutate(condition = "Sum") %>%
      dplyr::select(condition, race_cat, n)
  }
  
  if(datasource %in% c("study 1", "study 2", "study 3")) {
    race <- race %>%
    mutate(condition = factor(ifelse(condition == "Sum", 
                                     "all", as.character(condition)),
                              levels = c("beetle", "robot", "all"))) %>%
    arrange(condition, race_cat) %>%
    spread(race_cat, n)
  }
  
  if(datasource == "study 4") {
    race <- race %>%
    mutate(condition = factor(ifelse(condition == "Sum", 
                                     "all", as.character(condition)),
                              levels = c(levels(d$condition), "all"))) %>%
    arrange(condition, race_cat) %>%
    spread(race_cat, n)
  }
  
  # return dataframe for using kable
  return(race)
}
demoReligion <- function(datasource) {

  # set target dataset
  if(datasource == "study 1"){d <- d1}
  if(datasource == "study 2"){d <- d2}
  if(datasource == "study 3"){d <- d3}

  # get religion per condition and overall
  if(datasource %in% c("study 1", "study 2")) {
    religion <- data.frame(addmargins(with(d, table(condition, religion_cat)))) %>%
      filter(religion_cat != "Sum") %>%
      rename(n = Freq)
  }
  
  if(datasource == "study 3") {
    religion <- data.frame(with(d, table(religion_cat))) %>%
      rename(n = Freq) %>%
      mutate(condition = "Sum") %>%
      dplyr::select(condition, religion_cat, n)
  }
  
  if(datasource %in% c("study 1", "study 2", "study 3")) {
    religion <- religion %>%
    mutate(condition = factor(ifelse(condition == "Sum", 
                                     "all", as.character(condition)),
                              levels = c("beetle", "robot", "all"))) %>%
    arrange(condition, religion_cat) %>%
    spread(religion_cat, n)
  }
  
  # return dataframe for using kable
  if(datasource == "study 4"){
    stop("Religion information not available for Study 4")
  } else {return(religion)}
}
demoEducation <- function(datasource) {

  # set target dataset
  if(datasource == "study 4"){d <- d4}
  
  # get education per condition and overall
  if(datasource == "study 4") {
    education <- 
      data.frame(addmargins(with(d, table(condition, education)))) %>%
      filter(education != "Sum") %>%
      rename(n = Freq) %>%
      mutate(condition = factor(ifelse(condition == "Sum",
                                       "all", as.character(condition)),
                                levels = c(levels(d$condition), "all")),
             education = factor(education,
                                levels = c(1:7, 0),
                                labels = c("some_HS",
                                           "HS_diploma",
                                           "some_college",
                                           "associates",
                                           "bachelors",
                                           "some_grad",
                                           "grad",
                                           "pref_no"))) %>%
      arrange(condition, education) %>%
      spread(education, n)
  }
  
  # return dataframe for using kable
  if(datasource %in% c("study 1", "study 2", "study 3")){
    stop("Education information not available for Studies 1-3")
  } else {return(education)}
}

# plotting functions
makeFacetLabs <- function(df_plotting) {
  facet_labels <- array()
  df_plotting <- df_plotting %>% mutate(condition = factor(condition))
  for(i in 1:length(levels(df_plotting$condition))) {
    df <- df_plotting %>% filter(condition == levels(df_plotting$condition)[i]) %>%
      select(condition, n) %>% unique()
    facet_labels[i] <- paste0(df$condition, " (n = ", df$n, ")")
  }
  names(facet_labels) <- levels(df_plotting$condition)
  return(facet_labels)
}
```

```{r modeling decisions}
# remove outliers?
chosenOutlierHandling <- "keep"
# chosenOutlierHandling <- "remove"

# exclude any conditions in study 4?
chosenExclude <- "none"
# chosenExclude <- c("stapler", "car", "computer")

# exclude any items?
chosenExcludeItem <- "none"
# chosenExcludeItem <- "computations"

# NOTE: always choose minimal residual (fm = "minres") instead of ML because of non-normality

# for EFAs, what kind of correlation?
chosenCorType <- "cor" # pearson correlation
# chosenCorType <- "poly" # polychoric correlation

# for EFAs, what kind of rotation?
chosenRotType <- "varimax" # varimax rotation
# chosenRotType <- "oblimin" # oblimin rotation
# chosenRotType <- "none" # no rotation

data.frame("conditionsExcluded" = chosenExclude,
           "outlierHandling" = chosenOutlierHandling,
           "EFA_correlation" = chosenCorType,
           "EFA_rotation" = chosenRotType)
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

# Data preparation

```{r data upload}
# study 1 (2015-12-15, 2 conditions, between-subjects)
d_raw_study1 <- read.csv("https://osf.io/29vng/download") %>%
  mutate(study = "study 1")

# study 2 (2016-01-12, 2 conditions, between-subjects - REPLICATION)
d_raw_study2 <- read.csv("https://osf.io/g76hj/download") %>%
  mutate(study = "study 2")

# study 3 (2016-01-10, 2 conditions, within-subjects)
d_raw_study3 <- read.csv("https://osf.io/epykf/download") %>%
  mutate(study = "study 3")

# study 4 (2016-01-14, 21 conditions, between-subjects)
d_raw_study4 <- read.csv("https://osf.io/kdzge/download") %>%
  mutate(study = "study 4")
```

```{r data cleanup}
# clean up datasets
d1 <- cleanup("study 1")
d2 <- cleanup("study 2")
d3 <- cleanup("study 3")
d4 <- cleanup("study 4")
```

```{r dataframes for dimension reducation}
# make dataframes for s1
# d1_beetle <- makeDRDF("study 1", "beetle")
# d1_robot <- makeDRDF("study 1", "robot")
d1_all <- makeDRDF("study 1", "all")

# make dataframes for study 2
# d2_beetle <- makeDRDF("study 2", "beetle")
# d2_robot <- makeDRDF("study 2", "robot")
d2_all <- makeDRDF("study 2", "all")

# make dataframes for study 3
# d3_beetle <- makeDRDF("study 3", "beetle")
# d3_robot <- makeDRDF("study 3", "robot")
d3_all <- makeDRDF("study 3", "all")

# make dataframes for study 4
d4_all <- makeDRDF("study 4", "all")
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

# Analysis plan

For all studies we conduct exploratory factor analyses using Pearson correlations to find minimum residual solutions. 

For each study, we first examine maximal unrotated and rotated solutions. To determine the maximum number of factors to extract, we use the following rule of thumb: With $p$ observations per participant, we can extract a maximum of $k$ factors, where $(p-k)*2 > p+k$, i.e., $k < p/3$. Thus, with 40 mental capacity items, we can extract a maximum of 13 factors.

To determine how many factors to retain, we use the following preset retention criteria, considering the unrotated maximal solution (unless otherwise noted):

  - Each factor must have an eigenvalue >1.0.
  - Each factor must individually account for >5% of the total variance in the maximal model.
  - After rotation, each factor must be the dominant factor (i.e., the factor with the highest factor loading) for ≥1 mental capacity item.

We then examine and interpret varimax-rotated solutions, extracting only the number of factors that meet these criteria.

*Note*: For Studies 1-2, we initially planned to conduct dimension reduction analyses for each condition (beetle vs. robot) separately. However, we now consider this analysis plan to have been fundamentally flawed: Each of these separate analyses is only capable of surfacing factors that highlight substantial disagreement among participants within that condition thus failing to capture key differences in attributions of mental capacities to beetles vs. robots, with no formal means of synthesizing results across conditions. Nonetheless, the results of these analyses are generally consistent with the findings reported here: The most prominent and reliable finding within each condition is that participants distinguish between emotional and perceptual varieties of experience. See <a href="https://osf.io/zd3mu", target="blank">https://osf.io/zd3mu</a> for the preregistered analyses, including analysis scripts.

# Study 1

Design: 2 conditions (beetle, robot), between-subjects
Date conducted: 2015-12-15

## Demographics

```{r s1 demographics}
# examine exclusion
excludedCounts("study 1")

# make demographics tables
demoSampleSize("study 1")
demoDuration("study 1")
demoAge("study 1")
demoGender("study 1")
demoRace("study 1")
demoReligion("study 1")
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

## Exploratory factor analysis

### Step 1: Run maximal EFA (without and with rotation)

```{r s1 all no rotation}
# examine scree plot
# fa.parallel(d1_all)

# run EFA without rotation with N factors
efa_d1_all_unrotated <- fa(d1_all, 13, rotate = "none",
                           cor = chosenCorType, fm = "minres")
print(efa_d1_all_unrotated)

# examine eigenvalues and variance explained
efa_d1_all_unrotated_eigenvalues <- print(efa_d1_all_unrotated)$Vaccounted %>%
  t() %>%
  data.frame()

# count factors with eigenvalues > 1 and variance explained > 5%
efa_d1_all_unrotated_nfactors <- efa_d1_all_unrotated_eigenvalues %>%
  filter(SS.loadings > 1, Proportion.Explained > 0.05) %>%
  count() %>%
  as.numeric()
efa_d1_all_unrotated_nfactors
```

```{r s1 all rotation}
efa_d1_all_rotated_max <- fa(d1_all, 13, rotate = chosenRotType,
                           cor = chosenCorType, fm = "minres")

efa_d1_all_rotated <- fa(d1_all, efa_d1_all_unrotated_nfactors, rotate = chosenRotType,
                           cor = chosenCorType, fm = "minres")

# check that each of these factors is the dominant factor for at least one mental capacity item
efa_d1_all_rotated_loadings <- fa.sort(loadings(efa_d1_all_rotated)[]) %>%
  data.frame() %>%
  rownames_to_column("capacity") %>%
  gather(factor, loading, -capacity) %>%
  mutate(loading_abs = abs(loading)) %>%
  group_by(capacity) %>%
  top_n(1, loading_abs) %>%
  ungroup()
efa_d1_all_rotated_loadings

# drop any factors where n < 1
efa_d1_all_rotated_loadings %>% 
  count(factor) %>% 
  filter(n > 0)

# set number of factors to extract
nfactors_d1_all <- efa_d1_all_rotated_loadings %>% 
  count(factor) %>% 
  filter(n > 0) %>%
  nrow()
nfactors_d1_all
```

### Step 2: Run EFA with varimax rotation

```{r s1 all varimax rotation}
# run EFA with rotation with N factors
efa_d1_all_rotatedN <- fa(d1_all, nfactors_d1_all, 
                          rotate = chosenRotType, cor = chosenCorType, fm = "minres")
print(efa_d1_all_rotatedN)

# get loadings for each factor
efa_d1_all_rotatedN_loadings <- loadings(efa_d1_all_rotatedN)[] %>%
  data.frame() %>% 
  rownames_to_column(var = "mc")
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

#### Factor loadings table

```{r s1 loadings table}
data.frame(loadings(fa.sort(efa_d1_all_rotatedN))[]) %>%
  rownames_to_column("capacity") %>%
  mutate_at(vars(starts_with("M")), funs(round2))
```

# Study 2

Design: 2 conditions (beetle, robot), between-subjects (replication of Study 1)
Date conducted: 2016-01-12

## Demographics

```{r s2 demographics}
# examine exclusion
excludedCounts("study 2")

# make demographics tables
demoSampleSize("study 2")
demoDuration("study 2")
demoAge("study 2")
demoGender("study 2")
demoRace("study 2")
demoReligion("study 2")
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

## Exploratory factor analysis

### Step 1: Run maximal EFA (without and with rotation)

```{r s2 all no rotation}
# examine scree plot
# fa.parallel(d2_all)

# run EFA without rotation with N factors
efa_d2_all_unrotated <- fa(d2_all, 13, rotate = "none",
                           cor = chosenCorType, fm = "minres")
print(efa_d2_all_unrotated)

# examine eigenvalues and variance explained
efa_d2_all_unrotated_eigenvalues <- print(efa_d2_all_unrotated)$Vaccounted %>%
  t() %>%
  data.frame()

# count factors with eigenvalues > 1 and variance explained > 5%
efa_d2_all_unrotated_nfactors <- efa_d2_all_unrotated_eigenvalues %>%
  filter(SS.loadings > 1, Proportion.Explained > 0.05) %>%
  count() %>%
  as.numeric()
efa_d2_all_unrotated_nfactors
```

```{r s2 all rotation}
efa_d2_all_rotated_max <- fa(d2_all, 13, rotate = chosenRotType,
                           cor = chosenCorType, fm = "minres")

efa_d2_all_rotated <- fa(d2_all, efa_d2_all_unrotated_nfactors, rotate = chosenRotType,
                           cor = chosenCorType, fm = "minres")

# check that each of these factors is the dominant factor for at least one mental capacity item
efa_d2_all_rotated_loadings <- fa.sort(loadings(efa_d2_all_rotated)[]) %>%
  data.frame() %>%
  rownames_to_column("capacity") %>%
  gather(factor, loading, -capacity) %>%
  mutate(loading_abs = abs(loading)) %>%
  group_by(capacity) %>%
  top_n(1, loading_abs) %>%
  ungroup()
efa_d2_all_rotated_loadings

# drop any factors where n < 1
efa_d2_all_rotated_loadings %>% 
  count(factor) %>% 
  filter(n > 0)

# set number of factors to extract
nfactors_d2_all <- efa_d2_all_rotated_loadings %>% 
  count(factor) %>% 
  filter(n > 0) %>%
  nrow()
nfactors_d2_all
```

### Step 2: Run EFA with varimax rotation

```{r s2 all varimax rotation}
# run EFA with rotation with N factors
efa_d2_all_rotatedN <- fa(d2_all, nfactors_d2_all, 
                          rotate = chosenRotType, cor = chosenCorType, fm = "minres")
print(efa_d2_all_rotatedN)

# get loadings for each factor
efa_d2_all_rotatedN_loadings <- loadings(efa_d2_all_rotatedN)[] %>%
  data.frame() %>% 
  rownames_to_column(var = "mc")
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

#### Factor loadings table

```{r s2 loadings table}
data.frame(loadings(fa.sort(efa_d2_all_rotatedN))[]) %>%
  rownames_to_column("capacity") %>%
  mutate_at(vars(starts_with("M")), funs(round2))
```

# Study 3 

Design: 2 conditions (beetle, robot), within-subjects
Date conducted: 2016-01-10

## Demographics

```{r s3 demographics}
# examine exclusion
excludedCounts("study 3")

# make demographics tables
demoSampleSize("study 3")
demoDuration("study 3")
demoAge("study 3")
demoGender("study 3")
demoRace("study 3")
demoReligion("study 3")
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

## Exploratory factor analysis

### Step 1: Run maximal EFA (without and with rotation)

```{r s3 all no rotation}
# examine scree plot
# fa.parallel(d3_all)

# run EFA without rotation with N factors
efa_d3_all_unrotated <- fa(d3_all, 13, rotate = "none",
                           cor = chosenCorType, fm = "minres")
print(efa_d3_all_unrotated)

# examine eigenvalues and variance explained
efa_d3_all_unrotated_eigenvalues <- print(efa_d3_all_unrotated)$Vaccounted %>%
  t() %>%
  data.frame()

# count factors with eigenvalues > 1 and variance explained > 5%
efa_d3_all_unrotated_nfactors <- efa_d3_all_unrotated_eigenvalues %>%
  filter(SS.loadings > 1, Proportion.Explained > 0.05) %>%
  count() %>%
  as.numeric()
efa_d3_all_unrotated_nfactors
```

```{r s3 all rotation}
efa_d3_all_rotated_max <- fa(d3_all, 13, rotate = chosenRotType,
                           cor = chosenCorType, fm = "minres")

efa_d3_all_rotated <- fa(d3_all, efa_d3_all_unrotated_nfactors, rotate = chosenRotType,
                           cor = chosenCorType, fm = "minres")

# check that each of these factors is the dominant factor for at least one mental capacity item
efa_d3_all_rotated_loadings <- fa.sort(loadings(efa_d3_all_rotated)[]) %>%
  data.frame() %>%
  rownames_to_column("capacity") %>%
  gather(factor, loading, -capacity) %>%
  mutate(loading_abs = abs(loading)) %>%
  group_by(capacity) %>%
  top_n(1, loading_abs) %>%
  ungroup()
efa_d3_all_rotated_loadings

# drop any factors where n < 1
efa_d3_all_rotated_loadings %>% 
  count(factor) %>% 
  filter(n > 0)

# set number of factors to extract
nfactors_d3_all <- efa_d3_all_rotated_loadings %>% 
  count(factor) %>% 
  filter(n > 0) %>%
  nrow()
nfactors_d3_all
```

### Step 2: Run EFA with varimax rotation

```{r s3 all varimax rotation}
# run EFA with rotation with N factors
efa_d3_all_rotatedN <- fa(d3_all, nfactors_d3_all, 
                          rotate = chosenRotType, cor = chosenCorType, fm = "minres")
print(efa_d3_all_rotatedN)

# get loadings for each factor
efa_d3_all_rotatedN_loadings <- loadings(efa_d3_all_rotatedN)[] %>%
  data.frame() %>% 
  rownames_to_column(var = "mc")
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

#### Factor loadings table

```{r s3 loadings table}
data.frame(loadings(fa.sort(efa_d3_all_rotatedN))[]) %>%
  rownames_to_column("capacity") %>%
  mutate_at(vars(starts_with("M")), funs(round2))
```

# Study 4

Design: 21 conditions, between-subjects
Date conducted: 2016-01-14

## Demographics

```{r s4 demographics}
# examine exclusion
excludedCounts("study 4")

# make demographics tables
demoSampleSize("study 4")
demoDuration("study 4")
demoAge("study 4")
demoGender("study 4")
demoRace("study 4")
demoEducation("study 4")
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

## Exploratory factor analysis

### Step 1: Run maximal EFA (without and with rotation)

```{r s4 all no rotation}
# examine scree plot
# fa.parallel(d4_all)

# run EFA without rotation with N factors
efa_d4_all_unrotated <- fa(d4_all, 13, rotate = "none",
                           cor = chosenCorType, fm = "minres")
print(efa_d4_all_unrotated)

# examine eigenvalues and variance explained
efa_d4_all_unrotated_eigenvalues <- print(efa_d4_all_unrotated)$Vaccounted %>%
  t() %>%
  data.frame()

# count factors with eigenvalues > 1 and variance explained > 5%
efa_d4_all_unrotated_nfactors <- efa_d4_all_unrotated_eigenvalues %>%
  filter(SS.loadings > 1, Proportion.Explained > 0.05) %>%
  count() %>%
  as.numeric()
efa_d4_all_unrotated_nfactors
```

```{r s4 all rotation}
efa_d4_all_rotated_max <- fa(d4_all, 13, rotate = chosenRotType,
                           cor = chosenCorType, fm = "minres")

efa_d4_all_rotated <- fa(d4_all, efa_d4_all_unrotated_nfactors, rotate = chosenRotType,
                           cor = chosenCorType, fm = "minres")

# check that each of these factors is the dominant factor for at least one mental capacity item
efa_d4_all_rotated_loadings <- fa.sort(loadings(efa_d4_all_rotated)[]) %>%
  data.frame() %>%
  rownames_to_column("capacity") %>%
  gather(factor, loading, -capacity) %>%
  mutate(loading_abs = abs(loading)) %>%
  group_by(capacity) %>%
  top_n(1, loading_abs) %>%
  ungroup()
efa_d4_all_rotated_loadings

# drop any factors where n < 1
efa_d4_all_rotated_loadings %>% 
  count(factor) %>% 
  filter(n > 0)

# set number of factors to extract
nfactors_d4_all <- efa_d4_all_rotated_loadings %>% 
  count(factor) %>% 
  filter(n > 0) %>%
  nrow()
nfactors_d4_all
```

### Step 2: Run EFA with varimax rotation

```{r s4 all varimax rotation}
# run EFA with rotation with N factors
efa_d4_all_rotatedN <- fa(d4_all, nfactors_d4_all, 
                          rotate = chosenRotType, cor = chosenCorType, fm = "minres")
print(efa_d4_all_rotatedN)

# get loadings for each factor
efa_d4_all_rotatedN_loadings <- loadings(efa_d4_all_rotatedN)[] %>%
  data.frame() %>% 
  rownames_to_column(var = "mc")
```

<p style="text-align:right"><a href="#header">back to TOC</a></p>

#### Factor loadings table

```{r s4 loadings table}
data.frame(loadings(fa.sort(efa_d4_all_rotatedN))[]) %>%
  rownames_to_column("capacity") %>%
  mutate_at(vars(starts_with("M")), funs(round2))
```

# Big factor loadings table for all studies (Studies 1-4)

```{r all studies loadings table}
order_s1 <- loadings(fa.sort(efa_d1_all_rotatedN))[] %>%
  data.frame() %>%
  rownames_to_column(var = "mc") %>%
  rownames_to_column(var = "order1") %>%
  rename(s1_MR2 = MR2, s1_MR1 = MR1, s1_MR3 = MR3)

order_s2 <- loadings(fa.sort(efa_d2_all_rotatedN))[] %>%
  data.frame() %>%
  rownames_to_column(var = "mc") %>%
  rename(s2_MR2 = MR2, s2_MR1 = MR1, s2_MR3 = MR3)

order_s3 <- loadings(fa.sort(efa_d3_all_rotatedN))[] %>%
  data.frame() %>%
  rownames_to_column(var = "mc") %>%
  rename(s3_MR2 = MR2, s3_MR1 = MR1, s3_MR3 = MR3)

order_s4 <- loadings(fa.sort(efa_d4_all_rotatedN))[] %>%
  data.frame() %>%
  rownames_to_column(var = "mc") %>%
  rename(s4_MR2 = MR2, s4_MR1 = MR1, s4_MR3 = MR3)

bigTable <- order_s1 %>%
  full_join(order_s2) %>%
  full_join(order_s3) %>%
  full_join(order_s4) %>%
  mutate_at(vars(starts_with("s")), funs(round2)) %>%
  select(order1, mc, ends_with("MR1"), ends_with("MR2"), ends_with("MR3"))

bigTable
```

# Figures

```{r plotting setup character means}
# bootstrap 95% CIs for ratings by character (nonparametric)
# study 1
char_plotting_ratings_s1 <- d1 %>% 
  select(condition, subid, happy:pride) %>%
  gather(mc, response, -subid, -condition) %>%
  mutate(response = as.numeric(response)) %>%
  group_by(condition, mc) %>%
  do(data.frame(rbind(smean.cl.boot(.$response)))) %>%
  full_join(demoSampleSize("study 1") %>% filter(condition != "all")) %>%
  mutate(study = "study 1")

# study 2
char_plotting_ratings_s2 <- d2 %>% 
  select(condition, subid, happy:pride) %>%
  gather(mc, response, -subid, -condition) %>%
  mutate(response = as.numeric(response)) %>%
  group_by(condition, mc) %>%
  do(data.frame(rbind(smean.cl.boot(.$response)))) %>%
  full_join(demoSampleSize("study 2") %>% filter(condition != "all")) %>%
  mutate(study = "study 2")


# study 3
char_plotting_ratings_s3 <- d3 %>% 
  select(target, subid, happy:pride) %>%
  rename(condition = target) %>%
  gather(mc, response, -subid, -condition) %>%
  mutate(response = as.numeric(response)) %>%
  group_by(condition, mc) %>%
  do(data.frame(rbind(smean.cl.boot(.$response)))) %>%
  full_join(demoSampleSize("study 3") %>% filter(condition != "all")) %>%
  mutate(study = "study 3")

# study 4
char_plotting_ratings_s4 <- d4 %>% 
  select(condition, subid, happy:pride) %>%
  gather(mc, response, -subid, -condition) %>%
  mutate(response = as.numeric(response)) %>%
  group_by(condition, mc) %>%
  do(data.frame(rbind(smean.cl.boot(.$response)))) %>%
  full_join(demoSampleSize("study 4") %>% filter(condition != "all")) %>%
  mutate(study = "study 4")
```

```{r plotting setup character means merge}
char_plotting_ratings_all <- char_plotting_ratings_s1 %>%
  full_join(char_plotting_ratings_s2) %>%
  full_join(char_plotting_ratings_s3) %>%
  full_join(char_plotting_ratings_s4) %>%
  ungroup() %>%
  mutate(study = factor(study),
         condition = factor(condition,
                            levels = c("stapler", "car", "computer", "robot",
                                       "microbe", "beetle", "fish", "bluejay",
                                       "frog", "mouse", "goat", "dog",
                                       "bear", "dolphin", "elephant", "chimp",
                                       "fetus", "pvs", "infant", "child", "adult")))
```

```{r plotting setup capacity wordings}
char_plotting_wordings <- char_plotting_ratings_all %>%
  ungroup() %>%
  select(mc) %>%
  distinct() %>% 
  mutate(wording = factor(
    recode(mc,
           hungry = "getting hungry", pain = "experiencing pain",
           tired = "feeling tired", fear = "experiencing fear",
           computations = "doing computations", pleasure = "experiencing pleasure",
           conscious = "being conscious", free_will = "having free will",
           safe = "feeling safe", desires = "having desires",
           calm = "feeling calm", nauseated = "feeling nauseated",
           angry = "getting angry", intentions = "having intentions",
           self_aware = "being self-aware", odors = "detecting odors",
           embarrassed = "feeling embarrassed", pride = "experiencing pride",
           love = "feeling love", guilt = "experiencing guilt",
           depressed = "feeling depressed", disrespected = "feeling disrespected",
           beliefs = "holding beliefs", emo_recog = "understanding ... feeling",
           joy = "experiencing joy", personality = "having a personality",
           happy = "feeling happy", morality = "telling right from wrong",
           thoughts = "having thoughts", self_restraint = "exercising self-restraint",
           remembering = "remembering things", recognizing = "recognizing others",
           temperature = "sensing temperatures", communicating = "communicating ...",
           goal = "working toward a goal", depth = "perceiving depth",
           sounds = "detecting sounds", seeing = "seeing things",
           choices = "making choices", reasoning = "reasoning about things")))
```


```{r plotting setup loadings}
# merge with loadings, orderings, and dominant factors from each study 
char_plotting <- char_plotting_ratings_all %>%
  full_join(char_plotting_wordings) %>%
  full_join(order_s1 %>%
              mutate(s1_MR1_abs = abs(s1_MR1),
                     s1_MR2_abs = abs(s1_MR2),
                     s1_MR3_abs = abs(s1_MR3),
                     s1_factor = 
                       ifelse(s1_MR1_abs > s1_MR2_abs &
                                s1_MR1_abs > s1_MR3_abs, "BODY",
                              ifelse(s1_MR2_abs > s1_MR1_abs &
                                       s1_MR2_abs > s1_MR3_abs, "HEART",
                                     ifelse(s1_MR3_abs > s1_MR1_abs &
                                              s1_MR3_abs > s1_MR2_abs, "MIND",
                                            NA))),
                     s1_color = recode(s1_factor,
                                       "BODY" = "#377EB8",
                                       "HEART" = "#4DAF4A",
                                       "MIND" = "#E41A1C"),
                     s1_order = as.numeric(order1)) %>%
              select(-s1_MR1_abs, -s1_MR2_abs, -s1_MR3_abs)) %>%
  full_join(order_s2 %>%
              data.frame() %>%
              mutate(s2_MR1_abs = abs(s2_MR1),
                     s2_MR2_abs = abs(s2_MR2),
                     s2_MR3_abs = abs(s2_MR3),
                     s2_factor = 
                       ifelse(s2_MR1_abs > s2_MR2_abs &
                                s2_MR1_abs > s2_MR3_abs, "BODY",
                              ifelse(s2_MR2_abs > s2_MR1_abs &
                                       s2_MR2_abs > s2_MR3_abs, "HEART",
                                     ifelse(s2_MR3_abs > s2_MR1_abs &
                                              s2_MR3_abs > s2_MR2_abs, "MIND",
                                            NA))),
                     s2_color = recode(s2_factor,
                                       "BODY" = "#377EB8",
                                       "HEART" = "#4DAF4A",
                                       "MIND" = "#E41A1C")) %>%
              rownames_to_column(var = "s2_order") %>%
              mutate(s2_order = as.numeric(s2_order)) %>%
              select(-s2_MR1_abs, -s2_MR2_abs, -s2_MR3_abs)) %>%
  full_join(order_s3 %>%
              mutate(s3_MR1_abs = abs(s3_MR1),
                     s3_MR2_abs = abs(s3_MR2),
                     s3_MR3_abs = abs(s3_MR3),
                     s3_factor = 
                       ifelse(s3_MR1_abs > s3_MR2_abs &
                                s3_MR1_abs > s3_MR3_abs, "BODY",
                              ifelse(s3_MR2_abs > s3_MR1_abs &
                                       s3_MR2_abs > s3_MR3_abs, "HEART",
                                     ifelse(s3_MR3_abs > s3_MR1_abs &
                                              s3_MR3_abs > s3_MR2_abs, "MIND",
                                            NA))),
                     s3_color = recode(s3_factor,
                                       "BODY" = "#377EB8",
                                       "HEART" = "#4DAF4A",
                                       "MIND" = "#E41A1C")) %>%
              rownames_to_column(var = "s3_order") %>%
              mutate(s3_order = as.numeric(s3_order)) %>%
              select(-s3_MR1_abs, -s3_MR2_abs, -s3_MR3_abs)) %>%
  full_join(order_s4 %>%
              mutate(s4_MR1_abs = abs(s4_MR1),
                     s4_MR2_abs = abs(s4_MR2),
                     s4_MR3_abs = abs(s4_MR3),
                     s4_factor = 
                       ifelse(s4_MR1_abs > s4_MR2_abs &
                                s4_MR1_abs > s4_MR3_abs, "BODY",
                              ifelse(s4_MR2_abs > s4_MR1_abs &
                                       s4_MR2_abs > s4_MR3_abs, "HEART",
                                     ifelse(s4_MR3_abs > s4_MR1_abs &
                                              s4_MR3_abs > s4_MR2_abs, "MIND",
                                            NA))),
                     s4_color = recode(s4_factor,
                                       "BODY" = "#377EB8",
                                       "HEART" = "#4DAF4A",
                                       "MIND" = "#E41A1C")) %>%
              rownames_to_column(var = "s4_order") %>%
              mutate(s4_order = as.numeric(s4_order)) %>%
              select(-s4_MR1_abs, -s4_MR2_abs, -s4_MR3_abs))

# configure plot labels
label_df_s1 <- char_plotting %>% filter(study == "study 1") %>% select(condition, n) %>% unique()
label_df_s2 <- char_plotting %>% filter(study == "study 2") %>% select(condition, n) %>% unique()
label_df_s3 <- char_plotting %>% filter(study == "study 3") %>% select(condition, n) %>% unique()
label_df_s4 <- char_plotting %>% filter(study == "study 4") %>% select(condition, n) %>% unique()

facetLabs_s1 <- gsub(" \\(", "\n(", makeFacetLabs(char_plotting %>% filter(study == "study 1")))
facetLabs_s2 <- gsub(" \\(", "\n(", makeFacetLabs(char_plotting %>% filter(study == "study 2")))
facetLabs_s3 <- gsub(" \\(", "\n(", makeFacetLabs(char_plotting %>% filter(study == "study 3")))
facetLabs_s4 <- gsub(" \\(", "\n(", makeFacetLabs(char_plotting %>% filter(study == "study 4")))

# configure custom palette
myPalette <- brewer.pal(3, "Set1"); names(myPalette) <- c("BODY", "HEART", "MIND")

palette_base <- char_plotting %>% 
  select(mc, ends_with("_factor")) %>%
  distinct()
palette_s1 <- c(rep(myPalette["MIND"], palette_base %>% filter(s1_factor == "MIND") %>% count()),
                rep(myPalette["HEART"], palette_base %>% filter(s1_factor == "HEART") %>% count()),
                rep(myPalette["BODY"], palette_base %>% filter(s1_factor == "BODY") %>% count()))
palette_s2 <- c(rep(myPalette["MIND"], palette_base %>% filter(s2_factor == "MIND") %>% count()),
                rep(myPalette["HEART"], palette_base %>% filter(s2_factor == "HEART") %>% count()),
                rep(myPalette["BODY"], palette_base %>% filter(s2_factor == "BODY") %>% count()))
palette_s3 <- c(rep(myPalette["MIND"], palette_base %>% filter(s3_factor == "MIND") %>% count()),
                rep(myPalette["HEART"], palette_base %>% filter(s3_factor == "HEART") %>% count()),
                rep(myPalette["BODY"], palette_base %>% filter(s3_factor == "BODY") %>% count()))
palette_s4 <- c(rep(myPalette["MIND"], palette_base %>% filter(s4_factor == "MIND") %>% count()),
                rep(myPalette["HEART"], palette_base %>% filter(s4_factor == "HEART") %>% count()),
                rep(myPalette["BODY"], palette_base %>% filter(s4_factor == "BODY") %>% count()))
```

## Figure 1

Mean ratings of 40 mental capacities for a subset of the 21 entities included in Study 4. (See Fig. S5 for mean ratings for the full set of entities.) Participants responded on a scale from 0 (Not at all capable) to 6 (Highly capable). Error bars are nonparametric bootstrapped 95% confidence intervals. Mental capacities are grouped according to their dominant factor loading in Study 1. *Doing computations* was the only item to load negatively on its dominant factor in any study (and did so in Studies 1-3); in Study 4, it loaded positively on its dominant factor (Factor 2, “heart”).

```{r figure 1}
# make dataframe
s4_subset_plotting <- char_plotting %>% 
  filter(study == "study 4",
         condition %in% c("stapler", "robot", "beetle", "goat", "elephant", "adult")) %>%
  distinct()

# plot! (ordered by study 1 factor loadings)
s4_subset <- ggplot(s4_subset_plotting, 
               aes(x = Mean, y = reorder(wording, desc(s1_order)), colour = s1_color)) +
  geom_point(stat = "identity", position = "identity", size = 2) +
  geom_errorbarh(aes(xmin = Lower, xmax = Upper), height = 0.4) +
  facet_wrap(~ condition, ncol = 6,
             labeller = labeller(condition = facetLabs_s4)) +
  theme_bw() +
  theme(text = element_text(size = 9),
        axis.title.y = element_blank(),
        axis.text.y = element_text(face = "italic",
                                   colour = palette_s1),
        panel.grid.minor = element_blank(),
        legend.position = "none") +
  scale_x_continuous(name = "\nMean rating",
                     limits = c(-3, 3),
                     breaks = seq(-3, 3, 1),
                     labels = seq(0, 6, 1)) +
  scale_colour_brewer(name = "Factor:",
                      type = "qual", palette = 6)

s4_subset
```

## Figure S1 (supporting materials)

Factor loadings for the 40 mental capacities on the three rotated factors in Study 1. Items are colored by their dominant factor loading: Items that loaded most strongly on the body factor (physiological states and will) are in red; items that loaded most strongly on the heart factor (social-emotional experiences and morality) are in blue; and items that loaded most strongly on the mind factor (perceptual-cognitive abilities and goal pursuit) are in green.

```{r figure S1}
# set up labels for plot (shortened version of mental capacity items)
wording <- loadings(efa_d1_all_rotatedN)[] %>%
  data.frame() %>%
  rownames_to_column(var = "item") %>%
  select(item) %>%
  mutate(wording = c("feeling happy", "feeling depressed", "experiencing fear",
                     "getting angry", "feeling calm", "detecting sounds",
                     "seeing things", "sensing temperatures", "detecting odors",
                     "perceiving depth", "doing computations", "having thoughts",
                     "reasoning about things", "remembering things", "holding beliefs",
                     "getting hungry", "feeling tired", "experiencing pain",
                     "feeling nauseated", "feeling safe", "feeling love",
                     "recognizing someone", "communicating with others", "experiencing guilt",
                     "feeling disrespected", "having free will", "making choices",
                     "exercising self-restraint", "having intentions", "working toward a goal",
                     "being conscious", "being self-aware", "having desires",
                     "feeling embarrassed", "understanding how others are feeling", 
                     "experiencing joy", "telling right from wrong", "having a personality",
                     "experiencing pleasure", "experiencing pride"),
         short = c("happiness", "depression", "fear",
                   "anger", "calm", "sound",
                   "sight", "temperature", "odor",
                   "depth", "computation", "thought",
                   "reasoning", "memory", "belief",
                   "hunger", "tiredness", "pain",
                   "nausea", "safety", "love",
                   "recognition", "communication", "guilt",
                   "disrespect", "free will", "choice",
                   "self-restraint", "intention", "goal",
                   "consciousness", "self-awareness", "desire",
                   "embarrassment", "empathy", 
                   "joy", "morality", "personality",
                   "pleasure", "pride"))

# make dataframe for plotting
scatter_plotting <- loadings(efa_d1_all_rotatedN)[] %>%
  data.frame() %>%
  rownames_to_column(var = "item") %>%
  rename(BODY = MR1,
         HEART = MR2,
         MIND = MR3) %>%
  full_join(wording) %>%
  mutate(dominant = factor(
    ifelse(pmax(abs(BODY), abs(HEART), abs(MIND)) == abs(BODY), "BODY",
           ifelse(pmax(abs(BODY), abs(HEART), abs(MIND)) == abs(HEART), "HEART",
                  ifelse(pmax(abs(BODY), abs(HEART), abs(MIND)) == abs(MIND), "MIND",
                         NA)))),
    size = ifelse(pmax(abs(BODY), abs(HEART), abs(MIND)) == abs(BODY), abs(BODY),
                  ifelse(pmax(abs(BODY), abs(HEART), abs(MIND)) == abs(HEART), abs(HEART),
                         ifelse(pmax(abs(BODY), abs(HEART), abs(MIND)) == abs(MIND), abs(MIND),
                                NA))),
    color = ifelse(dominant == "BODY", "#e41a1c",
                   ifelse(dominant == "HEART", "#377eb8",
                          ifelse(dominant == "MIND", "#4daf4a",
                                 NA))))

# plot!
figS1 <- plot_ly(data = scatter_plotting, 
                 x = ~HEART, y = ~BODY, z = ~MIND,
                 type = "scatter3d",
                 color = ~dominant, colors = c("#4daf4a", "#e41a1c", "#377eb8"),
                 marker = list(size = 4),
                 text = ~short,
                 textfont = list(size = 15),
                 mode = "text+markers",
                 showlegend = TRUE)

figS1
```

## Figure S2 (supporting materials)

Mean ratings of 40 mental capacities for the 2 entities included in Studies 1-3. Participants responded on a scale from 0 (Not at all capable) to 6 (Highly capable). Error bars are nonparametric bootstrapped 95% confidence intervals. Mental capacities are grouped according to their dominant factor loading in Study 1.

```{r figure S2}
# make dataframe
s123_plotting <- char_plotting %>% 
  filter(study != "study 4") %>%
  distinct()

# plot! (ordered by study 3 factor loadings)
s123 <- ggplot(s123_plotting, 
               aes(y = Mean, x = reorder(wording, desc(s1_order)), colour = s1_color,
                   shape = study)) +
  geom_point(stat = "identity", position = position_dodge(width = 0.6), size = 2) +
  geom_errorbar(aes(ymin = Lower, ymax = Upper), width = 0.4,
                position = position_dodge(width = 0.6)) +
  facet_wrap(~ condition) +
  theme_bw() +
  scale_y_continuous(name = "\nMean rating",
                     limits = c(-3, 3),
                     breaks = seq(-3, 3, 1),
                     labels = seq(0, 6, 1)) +
  scale_shape_discrete(name = "Study:") +
  scale_colour_brewer(name = "Factor:",
                      type = "qual", palette = 6,
                      guide = FALSE) +
  coord_flip() +
  theme(text = element_text(size = 9),
      axis.title.y = element_blank(),
      axis.text.y = element_text(face = "italic",
                                 colour = palette_s1),
      panel.grid.minor = element_blank(),
      legend.position = "right")

s123
```

## Figure S3 (supporting materials)

Mean ratings of 40 mental capacities for all 21 entities included in Study 4. Participants responded on a scale from 0 (Not at all capable) to 6 (Highly capable). Error bars are nonparametric bootstrapped 95% confidence intervals. Mental capacities are grouped according to their dominant factor loading in Study 4.

```{r figure S3}
# make dataframe
s4_plotting <- char_plotting %>% 
  filter(study == "study 4") %>%
  distinct()

# plot! (ordered by study 4 factor loadings)
s4 <- ggplot(s4_plotting, 
               aes(x = Mean, y = reorder(wording, desc(s4_order)), colour = s4_color)) +
  geom_point(stat = "identity", position = "identity", size = 1) +
  geom_errorbarh(aes(xmin = Lower, xmax = Upper), height = 0.01) +
  facet_wrap(~ condition, ncol = 21,
             labeller = labeller(condition = facetLabs_s4)) +
  theme_bw() +
  theme(text = element_text(size = 6),
        axis.title.y = element_blank(),
        axis.text.y = element_text(face = "italic",
                                   colour = palette_s4),
        panel.grid.minor = element_blank(),
        legend.position = "none") +
  scale_x_continuous(name = "\nMean rating",
                     limits = c(-3, 3),
                     breaks = seq(-3, 3, 1),
                     labels = seq(0, 6, 1)) +
  scale_colour_brewer(name = "Factor:",
                      type = "qual", palette = 6)

s4
```

